Systems, devices, and methods for non-invasive image-based plaque analysis and risk determination

ABSTRACT

Systems and methods of facilitating determination of risk of coronary artery disease (CAD) based at least in part on one or more measurements derived from non-invasive medical image analysis. The methods can include accessing a non-invasive generated medical image, identifying one or more arteries, identifying, regions of plaque within an artery, analyzing the regions of plaque to identify low density non-calcified plaque, non-calcified plaque, or calcified plaque based at least in part on density, determining a distance from identified regions of low density non-calcified plaque to one or more of a lumen wall or vessel wall, determining embeddedness of the regions of low density non-calcified plaque by one or more of non-calcified plaque or calcified plaque, determining a shape of the more regions of low density non-calcified plaque, and generating a display of the analysis to facilitate determination of one or more of a risk of CAD of the subject.

PRIORITY AND RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.18/179,921, filed Mar. 7, 2023, which claims priority to U.S.Provisional Application No. 63/269,136, filed Mar. 10, 2022, U.S.Provisional Application No. 63/362,108, filed Mar. 29, 2022, U.S.Provisional Application No. 63/362,856, filed Apr. 12, 2022, U.S.Provisional Application No. 63/364,078, filed May 3, 2022, U.S.Provisional Application No. 63/364,084, filed May 3, 2022, U.S.Provisional Application No. 63/365,381, filed May 26, 2022, U.S.Provisional Application No. 63/368,293 filed Jul. 13, 2022, and U.S.Provisional Application No. 63/381,210, filed Oct. 27, 2022, and each ofthe above-listed applications is incorporated by reference herein in itsentirety. Any and all applications for which a foreign or domesticpriority claim is identified in the Application Data Sheet as filed withthe present application are hereby incorporated by reference under 37CFR 1.57.

This application is related to U.S. Pat. No. 10,813,612, filed Jan. 23,2020, U.S. Pat. No. 11,501,436, filed Jan. 5, 2021, and U.S. Pat. No.11,302,001, filed Aug. 4, 2021, and U.S. application Ser. No.17/820,439, filed Aug. 17, 2022, and each of the above-listed patentsand patent applications is incorporated by reference herein in itsentirety.

BACKGROUND

The present application relates to non-invasive image-based plaqueanalysis and risk determination.

SUMMARY

Various embodiments described herein relate to systems, devices, andmethods for non-invasive image-based plaque analysis and riskdetermination. In particular, in some embodiments, the systems, devices,and methods described herein are related to analysis of one or moreregions of plaque, such as for example coronary plaque, based on one ormore distances, volumes, shapes, morphologies, embeddedness, and/or axesmeasurements. For example, in some embodiments, the systems, devices,and methods described herein are related to plaque analysis based on oneor more of distance between plaque and vessel wall, distance betweenplaque and lumen wall, length along longitudinal axis, length alonglatitudinal axis, volume of low density non-calcified plaque, volume oftotal plaque, a ratio(s) between volume of low density non-calcifiedplaque and volume of total plaque, embeddedness of low densitynon-calcified plaque, and/or the like. In some embodiments, the systems,devices, and methods described herein are configured to determine a riskof coronary artery disease (CAD), such as for example myocardialinfarction (MI), based on one or more plaque analyses described herein.In some embodiments, the systems, devices, and methods described hereinare configured to generate a proposed treatment and/or graphicalrepresentation based on the determined risk of CAD and/or one or moreplaque analyses described herein.

In some embodiments, the systems, devices, and methods are related tofacilitating determination of risk of coronary artery disease (CAD)based at least in part on one or more measurements derived fromnon-invasive medical image analysis. In some embodiments, the systems,devices, and methods comprise accessing a medical image of a subject,wherein the medical image of the subject is obtained non-invasively,analyzing medical image of the subject to identify one or more arteries,identifying one or more regions of plaque within the one or morecoronary arteries, analyzing the identified one or more regions ofplaque to identify one or more regions of low density non-calcifiedplaque, non-calcified plaque, or calcified plaque based at least in parton density, analyzing, in response to identifying one or more regions oflow density non-calcified plaque, the one or more regions of low densitynon-calcified plaque, wherein the analysis of the one or more regions oflow density non-calcified plaque comprises: determining a distance fromthe one or more regions of low density non-calcified plaque to one ormore of a lumen wall or vessel wall, determining a degree ofembeddedness of the one or more regions of low density non-calcifiedplaque in one or more of non-calcified plaque or calcified plaque, anddetermining a shape of the one or more regions of low densitynon-calcified plaque, and generating a display of the analysis of theone or more regions of low density non-calcified plaque to facilitatedetermination of a risk of CAD of the subject based at least in part onthe analysis of the one or more regions of low density non-calcifiedplaque.

In some embodiments, a determination of the distance from the one ormore regions of low density non-calcified plaque to the lumen wall belowa predetermined threshold is indicative of an unstable plaque or highrisk of CAD. In some embodiments, the distance from the one or moreregions of low density non-calcified plaque to one or more of the lumenwall or vessel wall is determined on a three-dimensional basis. In someembodiments, the distance from the one or more regions of low densitynon-calcified plaque to one or more of the lumen wall or vessel wall isdetermined based on a two-dimensional image. In some embodiments, thetwo-dimensional image is obtained by taking a two-dimensional sliceperpendicular to a longitudinal axis of a straightened multiplanar viewof the one or more arteries. In some embodiments, the two-dimensionalimage is obtained by taking a two-dimensional slice resulting in alargest two-dimensional area of the low-density non-calcified plaque. Insome embodiments, the distance from the one or more regions of lowdensity non-calcified plaque to the lumen wall is determined bydetermining a shortest distance between a boundary of the one or moreregions of low density non-calcified plaque and a boundary of the lumenwall. In some embodiments, the distance from the one or more regions oflow density non-calcified plaque to the vessel wall is determined bydetermining a shortest distance between a boundary of the one or moreregions of low density non-calcified plaque and a boundary of the vesselwall. In some embodiments, the one or more arteries comprises one ormore coronary or carotid arteries. In some embodiments, one or more axesof the one or more regions of low density non-calcified plaque comprisesone or more of a major axis on a longitudinal plane, minor axis on alongitudinal plane, major axis on a latitudinal plane, or minor axis ona latitudinal plane. In some embodiments, the one or more axes aredetermined on a three-dimensional basis. In some embodiments, the one ormore axes are determined based on one or more two-dimensional images. Insome embodiments, the longitudinal plane is obtained by taking atwo-dimensional slice parallel to a longitudinal axis of a straightenedmultiplanar view of the one or more arteries. In some embodiments, thelongitudinal plane is obtained by taking a two-dimensional sliceresulting in a longest major axis of the longitudinal plane. In someembodiments, the degree of embeddedness of the one or more regions oflow density non-calcified plaque is determined based at least in part bygraphically overlaying a protractor on the one or more regions of lowdensity non-calcified plaque on the medical image. In some embodiments,a higher degree of embeddedness of the one or more regions of lowdensity non-calcified plaque is indicative of an unstable plaque or highrisk of CAD. In some embodiments, the shape of the one or more regionsof low density non-calcified plaque is determined as one or more of acrescent, round, lobular, or bean shape. In some embodiments,determination of a round or bean shape of the one or more regions of lowdensity non-calcified plaque is indicative of an unstable plaque or highrisk of CAD. In some embodiments, the shape of the one or more regionsof low density non-calcified plaque is determined based at least in partby a machine learning algorithm. In some embodiments, the analysis ofthe one or more regions of low density non-calcified plaque furthercomprises determining one or more lengths of one or more axes of the oneor more regions of low density non-calcified plaque. In someembodiments, the shape of the one or more regions of low densitynon-calcified plaque is determined based at least in part on the one ormore determined lengths of the one or more axes. In some embodiments,the shape of the one or more regions of low density non-calcified plaqueis determined based at least in part on determining a standard deviationamong the one or more determined lengths of the one or more axes. Insome embodiments, the analysis of the one or more regions of low densitynon-calcified plaque further comprises determining a volume of the oneor more regions of low density non-calcified plaque, determining avolume of the one or more regions of plaque, and determining a ratio ofthe volume of the one or more regions of low density non-calcifiedplaque to the volume of the one or more regions of plaque. In someembodiments, the volume of the one or more regions of low densitynon-calcified plaque is determined based on the one or more determinedlengths of the one or more axes of the one or more regions of lowdensity non-calcified plaque. In some embodiments, a determination ofthe volume of the one or more regions of low density non-calcifiedplaque above a predetermined threshold is indicative of unstable plaqueor high risk of CAD. In some embodiments, a determination of the volumeof the one or more regions of plaque above a predetermined threshold isindicative of unstable plaque or high risk of CAD. In some embodiments,a determination of the ratio of the volume of the one or more regions oflow density non-calcified plaque to the volume of the one or moreregions of plaque above a predetermined threshold is indicative ofunstable plaque or high risk of CAD. In some embodiments, the densitycomprises absolute density. In some embodiments, the density comprisesradiodensity. In some embodiments, the medical image is obtained usingan imaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS).

Other embodiments disclosed herein relate to systems, devices, andmethods are related to cardiovascular risk and/or disease stateassessment using image-based analysis of vessel surface and/orcoordinates of features. In some embodiments, assessment ofcardiovascular risk and/or disease state generated using the systems,methods, and devices herein can be utilized to diagnose and/or generatea proposed treatment for a patient.

Other embodiments disclosed herein relate to systems, devices, andmethods are related to cardiovascular risk and/or disease and/or stateassessment using image-based analysis of one or more regions and/orfeatures of non-calcified plaque and/or calcified plaque. In someembodiments, assessment of cardiovascular risk and/or disease and/orstate generated using the systems, methods, and devices herein can beutilized to diagnose and/or generate a proposed treatment for a patient.

Other embodiments disclosed herein relate to systems, devices, andmethods are related to cardiovascular risk and/or disease and/or stateassessment using modified and/or normalized image analysis-based plaqueparameters. In some embodiments, assessment of cardiovascular riskand/or disease and/or state generated using the systems, methods, anddevices herein can be utilized to diagnose and/or generate a proposedtreatment for a patient.

Other embodiments disclosed herein relate to relate to systems, devices,and methods for generation of a patient-specific report on the riskand/or state assessment, diagnosis, and/or treatment of cardiovasculardisease, including for example coronary artery disease (CAD). Inparticular, in some embodiments, the systems, devices, and methods areconfigured to generate an immersive patient-specific report on thepatient's cardiovascular disease risk, state, diagnosis, and/ortreatment. In some embodiments, the systems, devices, and methods areconfigured to generate an immersive patient-specific report based atleast in part on image-based analysis, for example of one or more plaqueand/or vessel parameters.

Other embodiments disclosed herein relate to cardiovascular risk and/ordisease and/or state assessment using normalized image analysis-basedplaque parameters. In some embodiments, assessment of cardiovascularrisk and/or disease and/or state generated using the systems, methods,and devices herein can be utilized to diagnose and/or generate aproposed treatment for a patient.

Other embodiments disclosed herein relate to systems, devices, andmethods for non-invasive image-based determination of fractional flowreserve (FFR) and/or ischemia. In particular, in some embodiments, thesystems, devices, and methods are related to FFR and/or ischemiaanalysis of arteries, such as coronary, aortic, and/or carotid arteriesusing one or more image analysis techniques. For example, in someembodiments, the systems, methods, and devices can be configured toderive one or more stenosis and/or normal measurements from a medicalimage, which can be obtained non-invasively, and use the same to derivean assessment of FFR and/or ischemia. In some embodiments, the systems,methods, and devices can be configured to apply one or more allometricscaling laws to one or more stenosis and/or normal measurements toderive and/or generate an assessment of FFR and/or ischemia.

In some embodiments, a computer-implemented method of facilitatingdetermination of risk of coronary artery disease (CAD) based at least inpart on one or more measurements derived from non-invasive medical imageanalysis comprises: accessing, by a computer system, a medical image ofa subject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries; identifying, by thecomputer system, one or more regions of plaque within the one or morecoronary arteries; analyzing, by the computer system, the identified oneor more regions of plaque to identify one or more regions of low densitynon-calcified plaque, non-calcified plaque, or calcified plaque based atleast in part on density, analyzing, in response to identifying one ormore regions of low density non-calcified plaque, the one or moreregions of low density non-calcified plaque, wherein the analysis of theone or more regions of low density non-calcified plaque comprises:determining a distance from the one or more regions of low densitynon-calcified plaque to one or more of a lumen wall or vessel wall;determining a degree of embeddedness of the one or more regions of lowdensity non-calcified plaque by one or more of non-calcified plaque orcalcified plaque; and determining a shape of the one or more regions oflow density non-calcified plaque; and generating, by the computersystem, a display of the analysis of the one or more regions of lowdensity non-calcified plaque to facilitate determination of one or moreof a risk of CAD of the subject based at least in part on the analysisof the one or more regions of low density non-calcified plaque, whereinthe computer system comprises a computer processor and an electronicstorage medium.

In some embodiments of the computer-implemented method, a determinationof the distance from the one or more regions of low densitynon-calcified plaque to the lumen wall below a predetermined thresholdis indicative of an unstable plaque or high risk of CAD. In someembodiments of the computer-implemented method, the distance from theone or more regions of low density non-calcified plaque to one or moreof the lumen wall or vessel wall is determined on a three-dimensionalbasis. In some embodiments of the computer-implemented method, thedistance from the one or more regions of low density non-calcifiedplaque to one or more of the lumen wall or vessel wall is determinedbased on a two-dimensional image. In some embodiments of thecomputer-implemented method, the two-dimensional image is obtained bytaking a two-dimensional slice perpendicular to a longitudinal axis of astraightened multiplanar view of the one or more arteries. In someembodiments of the computer-implemented method, the two-dimensionalimage is obtained by taking a two-dimensional slice resulting in alargest two-dimensional area of the low-density non-calcified plaque. Insome embodiments of the computer-implemented method, the distance fromthe one or more regions of low density non-calcified plaque to the lumenwall is determined by determining a shortest distance between a boundaryof the one or more regions of low density non-calcified plaque and aboundary of the lumen wall. In some embodiments of thecomputer-implemented method, the distance from the one or more regionsof low density non-calcified plaque to the vessel wall is determined bydetermining a shortest distance between a boundary of the one or moreregions of low density non-calcified plaque and a boundary of the vesselwall. In some embodiments of the computer-implemented method, the one ormore arteries comprises one or more coronary or carotid arteries.

In some embodiments of the computer-implemented method, the one or moreaxes of the one or more regions of low density non-calcified plaquecomprises one or more of a major axis on a longitudinal plane, minoraxis on a longitudinal plane, major axis on a latitudinal plane, orminor axis on a latitudinal plane. In some embodiments of thecomputer-implemented method, the one or more axes are determined on athree-dimensional basis. In some embodiments of the computer-implementedmethod, the one or more axes are determined based on one or moretwo-dimensional images. In some embodiments of the computer-implementedmethod, the longitudinal plane is obtained by taking a two-dimensionalslice parallel to a longitudinal axis of a straightened multiplanar viewof the one or more arteries. In some embodiments of thecomputer-implemented method, the longitudinal plane is obtained bytaking a two-dimensional slice resulting in a longest major axis of thelongitudinal plane. In some embodiments of the computer-implementedmethod, the latitudinal plane is obtained by taking a two-dimensionalslice perpendicular to a longitudinal axis of a straightened multiplanarview of the one or more arteries. In some embodiments of thecomputer-implemented method, the latitudinal plane is obtained by takinga two-dimensional slice perpendicular to the major axis on thelongitudinal plane. In some embodiments of the computer-implementedmethod, the latitudinal plane is obtained by taking a two-dimensionalslice resulting in a largest two-dimensional area of the low-densitynon-calcified plaque. In some embodiments of the computer-implementedmethod, one or more of analyses of the one or more regions of lowdensity non-calcified plaque is performed by the computer system.

In some embodiments of the computer-implemented method, the degree ofembeddedness of the one or more regions of low density non-calcifiedplaque is determined based at least in part by graphically overlaying aprotractor on the one or more regions of low density non-calcifiedplaque on the medical image. In some embodiments of thecomputer-implemented method, a higher degree of embeddedness of the oneor more regions of low density non-calcified plaque is indicative of anunstable plaque or high risk of CAD. In some embodiments of thecomputer-implemented method, the shape of the one or more regions of lowdensity non-calcified plaque is determined as one or more of a crescent,round, lobular, or bean shape. In some embodiments of thecomputer-implemented method, determination of a round or bean shape ofthe one or more regions of low density non-calcified plaque isindicative of an unstable plaque or high risk of CAD. In someembodiments of the computer-implemented method, the shape of the one ormore regions of low density non-calcified plaque is determined based atleast in part by a machine learning algorithm.

In some embodiments of the computer-implemented method, the analysis ofthe one or more regions of low density non-calcified plaque furthercomprises determining one or more lengths of one or more axes of the oneor more regions of low density non-calcified plaque. In some embodimentsof the computer-implemented method, the shape of the one or more regionsof low density non-calcified plaque is determined based at least in parton the one or more determined lengths of the one or more axes. In someembodiments of the computer-implemented method, the shape of the one ormore regions of low density non-calcified plaque is determined based atleast in part on determining a standard deviation among the one or moredetermined lengths of the one or more axes. In some embodiments of thecomputer-implemented method, the analysis of the one or more regions oflow density non-calcified plaque further comprises: determining a volumeof the one or more regions of low density non-calcified plaque;determining a volume of the one or more regions of plaque; anddetermining a ratio of the volume of the one or more regions of lowdensity non-calcified plaque to the volume of the one or more regions ofplaque. In some embodiments of the computer-implemented method, thevolume of the one or more regions of low density non-calcified plaque isdetermined based on the one or more determined lengths of the one ormore axes of the one or more regions of low density non-calcifiedplaque. In some embodiments of the computer-implemented method, adetermination of the volume of the one or more regions of low densitynon-calcified plaque above a predetermined threshold is indicative ofunstable plaque or high risk of CAD. In some embodiments of thecomputer-implemented method, a determination of the volume of the one ormore regions of plaque above a predetermined threshold is indicative ofunstable plaque or high risk of CAD. In some embodiments of thecomputer-implemented method, a determination of the ratio of the volumeof the one or more regions of low density non-calcified plaque to thevolume of the one or more regions of plaque above a predeterminedthreshold is indicative of unstable plaque or high risk of CAD.

In some embodiments of the computer-implemented method, the densitycomprises absolute density. In some embodiments of thecomputer-implemented method, the density comprises radiodensity. In someembodiments of the computer-implemented method, the one or more regionsof plaque are identified as low density non-calcified plaque when aradiodensity value is between about −189 and about 30 Hounsfield units.In some embodiments of the computer-implemented method, the one or moreregions of plaque are identified as non-calcified plaque when aradiodensity value is between about 31 and about 350 Hounsfield units.In some embodiments of the computer-implemented method, the one or moreregions of plaque are identified as calcified plaque when a radiodensityvalue is between about 351 and 2500 Hounsfield units. In someembodiments of the computer-implemented method, the medical imagecomprises a Computed Tomography (CT) image. In some embodiments of thecomputer-implemented method, the medical image is obtained using animaging technique comprising one or more of CT, x-ray, ultrasound,echocardiography, MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS). In some embodiments of the computer-implementedmethod, the method further comprises generating, by the computer system,an assessment of risk of CAD of the subject or risk of the one or moreregions of plaque based at least in part on the analysis of the one ormore regions of low density non-calcified plaque. In some embodiments ofthe computer-implemented method, the method further comprisesgenerating, by the computer system, a recommended treatment for thesubject based at least in part on the analysis of the one or moreregions of low density non-calcified plaque.

In some embodiments, a system for facilitating determination of risk ofcoronary artery disease (CAD) based at least in part on one or moremeasurements derived from non-invasive medical image analysis comprises:one or more computer readable storage devices configured to store aplurality of computer executable instructions; and one or more hardwarecomputer processors in communication with the one or more computerreadable storage devices and configured to execute the plurality ofcomputer executable instructions in order to cause the system to: accessa medical image of a subject, wherein the medical image of the subjectis obtained non-invasively; analyze the medical image of the subject toidentify one or more arteries; identify one or more regions of plaquewithin the one or more coronary arteries; analyze the identified one ormore regions of plaque to identify one or more regions of low densitynon-calcified plaque, non-calcified plaque, or calcified plaque based atleast in part on density, facilitate analyzing, in response toidentifying one or more regions of low density non-calcified plaque, theone or more regions of low density non-calcified plaque, wherein theanalysis of the one or more regions of low density non-calcified plaquecomprises: determining a distance from the one or more regions of lowdensity non-calcified plaque to one or more of a lumen wall or vesselwall; determining a degree of embeddedness of the one or more regions oflow density non-calcified plaque by one or more of non-calcified plaqueor calcified plaque; and determining a shape of the one or more regionsof low density non-calcified plaque; and generate a display of theanalysis of the one or more regions of low density non-calcified plaqueto facilitate determination of one or more of a risk of CAD of thesubject based at least in part on the analysis of the one or moreregions of low density non-calcified plaque.

In some embodiments of the system, a determination of the distance fromthe one or more regions of low density non-calcified plaque to the lumenwall below a predetermined threshold is indicative of an unstable plaqueor high risk of CAD. In some embodiments of the system, the distancefrom the one or more regions of low density non-calcified plaque to oneor more of the lumen wall or vessel wall is determined on athree-dimensional basis. In some embodiments of the system, the distancefrom the one or more regions of low density non-calcified plaque to oneor more of the lumen wall or vessel wall is determined based on atwo-dimensional image. In some embodiments of the system, thetwo-dimensional image is obtained by taking a two-dimensional sliceperpendicular to a longitudinal axis of a straightened multiplanar viewof the one or more arteries. In some embodiments of the system, thetwo-dimensional image is obtained by taking a two-dimensional sliceresulting in a largest two-dimensional area of the low-densitynon-calcified plaque. In some embodiments of the system, the distancefrom the one or more regions of low density non-calcified plaque to thelumen wall is determined by determining a shortest distance between aboundary of the one or more regions of low density non-calcified plaqueand a boundary of the lumen wall. In some embodiments of the system, thedistance from the one or more regions of low density non-calcifiedplaque to the vessel wall is determined by determining a shortestdistance between a boundary of the one or more regions of low densitynon-calcified plaque and a boundary of the vessel wall. In someembodiments of the system, the one or more arteries comprises one ormore coronary or carotid arteries.

In some embodiments of the system, the one or more axes of the one ormore regions of low density non-calcified plaque comprises one or moreof a major axis on a longitudinal plane, minor axis on a longitudinalplane, major axis on a latitudinal plane, or minor axis on a latitudinalplane. In some embodiments of the system, the one or more axes aredetermined on a three-dimensional basis. In some embodiments of thesystem, the one or more axes are determined based on one or moretwo-dimensional images. In some embodiments of the system, thelongitudinal plane is obtained by taking a two-dimensional sliceparallel to a longitudinal axis of a straightened multiplanar view ofthe one or more arteries. In some embodiments of the system, thelongitudinal plane is obtained by taking a two-dimensional sliceresulting in a longest major axis of the longitudinal plane. In someembodiments of the system, the latitudinal plane is obtained by taking atwo-dimensional slice perpendicular to a longitudinal axis of astraightened multiplanar view of the one or more arteries. In someembodiments of the system, the latitudinal plane is obtained by taking atwo-dimensional slice perpendicular to the major axis on thelongitudinal plane. In some embodiments of the system, the latitudinalplane is obtained by taking a two-dimensional slice resulting in alargest two-dimensional area of the low-density non-calcified plaque. Insome embodiments of the system, one or more of analyses of the one ormore regions of low density non-calcified plaque is performed by thecomputer system.

In some embodiments of the system, the degree of embeddedness of the oneor more regions of low density non-calcified plaque is determined basedat least in part by graphically overlaying a protractor on the one ormore regions of low density non-calcified plaque on the medical image.In some embodiments of the system, a higher degree of embeddedness ofthe one or more regions of low density non-calcified plaque isindicative of an unstable plaque or high risk of CAD. In someembodiments of the system, the shape of the one or more regions of lowdensity non-calcified plaque is determined as one or more of a crescent,round, lobular, or bean shape. In some embodiments of the system,determination of a round or bean shape of the one or more regions of lowdensity non-calcified plaque is indicative of an unstable plaque or highrisk of CAD. In some embodiments of the system, the shape of the one ormore regions of low density non-calcified plaque is determined based atleast in part by a machine learning algorithm.

In some embodiments of the system, the analysis of the one or moreregions of low density non-calcified plaque further comprisesdetermining one or more lengths of one or more axes of the one or moreregions of low density non-calcified plaque. In some embodiments of thesystem, the shape of the one or more regions of low densitynon-calcified plaque is determined based at least in part on the one ormore determined lengths of the one or more axes. In some embodiments ofthe system, the shape of the one or more regions of low densitynon-calcified plaque is determined based at least in part on determininga standard deviation among the one or more determined lengths of the oneor more axes. In some embodiments of the system, the analysis of the oneor more regions of low density non-calcified plaque further comprises:determining a volume of the one or more regions of low densitynon-calcified plaque; determining a volume of the one or more regions ofplaque; and determining a ratio of the volume of the one or more regionsof low density non-calcified plaque to the volume of the one or moreregions of plaque. In some embodiments of the system, the volume of theone or more regions of low density non-calcified plaque is determinedbased on the one or more determined lengths of the one or more axes ofthe one or more regions of low density non-calcified plaque. In someembodiments of the system, a determination of the volume of the one ormore regions of low density non-calcified plaque above a predeterminedthreshold is indicative of unstable plaque or high risk of CAD. In someembodiments of the system, a determination of the volume of the one ormore regions of plaque above a predetermined threshold is indicative ofunstable plaque or high risk of CAD. In some embodiments of the system,a determination of the ratio of the volume of the one or more regions oflow density non-calcified plaque to the volume of the one or moreregions of plaque above a predetermined threshold is indicative ofunstable plaque or high risk of CAD.

In some embodiments of the system, the density comprises absolutedensity. In some embodiments of the system, the density comprisesradiodensity. In some embodiments of the system, the one or more regionsof plaque are identified as low density non-calcified plaque when aradiodensity value is between about −189 and about 30 Hounsfield units.In some embodiments of the system, the one or more regions of plaque areidentified as non-calcified plaque when a radiodensity value is betweenabout 31 and about 350 Hounsfield units. In some embodiments of thesystem, the one or more regions of plaque are identified as calcifiedplaque when a radiodensity value is between about 351 and 2500Hounsfield units. In some embodiments of the system, the medical imagecomprises a Computed Tomography (CT) image. In some embodiments of thesystem, the medical image is obtained using an imaging techniquecomprising one or more of CT, x-ray, ultrasound, echocardiography, MRimaging, optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS). In someembodiments of the system, the system is further caused to generate anassessment of risk of CAD of the subject or risk of the one or moreregions of plaque based at least in part on the analysis of the one ormore regions of low density non-calcified plaque. In some embodiments ofthe system, the system is further caused to generate a recommendedtreatment for the subject based at least in part on the analysis of theone or more regions of low density non-calcified plaque.

For purposes of this summary, certain aspects, advantages, and novelfeatures of the invention are described herein. It is to be understoodthat not necessarily all such advantages may be achieved in accordancewith any particular embodiment of the invention. Thus, for example,those skilled in the art will recognize that the invention may beembodied or carried out in a manner that achieves one advantage or groupof advantages as taught herein without necessarily achieving otheradvantages as may be taught or suggested herein.

All of these embodiments are intended to be within the scope of theinvention herein disclosed. These and other embodiments will becomereadily apparent to those skilled in the art from the following detaileddescription having reference to the attached figures, the invention notbeing limited to any particular disclosed embodiment(s).

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the devices and methods described herein willbe appreciated upon reference to the following description inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic of an example of an embodiment of a system100 that includes a processing system configured to characterizecoronary plaque.

FIG. 2 is a schematic illustrating an example of a heart muscle and itscoronary arteries.

FIG. 3 illustrates an example of a set of images generated from scanningalong a coronary artery, including a selected image of a portion of acoronary artery, and how image data may correspond to a value on theHounsfield Scale.

FIG. 4A is a block diagram that illustrates a computer system upon whichvarious embodiments may be implemented.

FIG. 4B is a block diagram that illustrates computer modules in acomputer system 400 which may implement various embodiments.

FIG. 5A illustrates an example of a flowchart of a process for analyzingcoronary plaque.

FIG. 5B illustrates an example of a flowchart that expands on a portionof the flowchart in FIG. 5A for determining characteristics of coronaryplaque.

FIG. 6 illustrates a representation of image data depicting an exampleof a portion of a coronary artery (sometimes referred to herein as a“vessel” for ease of reference).

FIG. 7 illustrates the same vessel and features of plaque and fat asillustrated in FIG. 6 and further illustrates additional examples ofareas of an artery, and the plaque and/or perivascular fat that is nearan artery, that may be analyzed to determine characteristics of apatient's arteries.

FIG. 8A is a block diagram that illustrates an example process ofidentifying features of medical images using artificial intelligence ormachine learning.

FIG. 8B is a schematic illustrating an example neural network that makesdeterminations about characteristics of a patient based on medicalimages.

FIG. 8C depicts a flow chart for training an artificial intelligence ormachine learning model according to some embodiments.

FIG. 8D illustrates an example of training and using an AI/ML modelaccording to some embodiments.

FIG. 9A illustrates examples of sample measurements, dimensions, and/oranalyses related to plaque analysis and risk determination, with FIG.9A(a) illustrating low-density non-calcified (LDNC) aggregatesassociated with a single coronary lesion, FIG. 9A(b) illustratingdistance to lumen and vessel walls, FIG. 9A(c) illustrating dimensionaxis along a major axis and along a minor axis, FIG. 9A(d) illustratingdegree of embeddedness of LDNC plaque (DELP), and FIG. 9A(e)illustrating four different shapes of plaques or regions of plaque,including crescent-shaped, round, lobular, and bean-shaped.

FIG. 9B illustrates example per-lesion high-risk plaque morphologycharacteristics derived from a sample study.

FIG. 9C illustrates example per-patient atherosclerotic characteristicsstratified by patients with and without an acute coronary syndrome eventderived from a sample study.

FIG. 9D illustrates example adjusted hazard ratios of the effect ofhigh-risk plaque morphology features on culprit lesion precursors toacute coronary syndrome derived from a sample study.

FIG. 9E illustrates an example Kaplan-Meier curve of occurrence of acutecoronary syndrome in patients with and without a high-risk plaquemorphology lesion derived from a sample study.

FIG. 9F is a flowchart illustrating an example embodiment(s) of systems,devices, and methods for non-invasive image-based plaque analysis andrisk determination.

FIG. 9G is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods described herein.

FIG. 9H illustrates an example embodiment(s) of assessing morphology oflow-density non-calcified plaque deposits, with FIG. 9H(a) illustratingexample LDNC deposits along a vessel, FIG. 9H(b) illustrating shapes orplaque or regions of plaque (e.g., crescent, lobular, round,bean-shaped), FIG. 9H(c) illustrating plaque having various values ofDELP (e.g., <180°, 180°-269°, 270°-359°, and 360°), and FIG. 9H(d)illustrating example high-risk plaque morphology).

FIG. 9I is a flowchart illustrating analyses of an example study datasetvalidating some embodiments of the systems, devices, and methods herein.

FIG. 9J illustrates patient demographics stratified by occurrence ofacute coronary syndrome in an example study validating some embodimentsof systems, methods, and devices described herein.

FIG. 9K illustrates per-lesion atherosclerotic characteristicsstratified by non-culprit and culprit lesion precursors in an examplestudy validating some embodiments of systems, methods, and devicesdescribed herein.

FIG. 9L is a forest plot of hazard ratios of risk for culprit lesionprecursors in an example study validating some embodiments of systems,methods, and devices described herein.

FIG. 9M illustrates per-patient atherosclerotic characteristicsstratified by patients with and without occurrence of acute coronarysyndrome in an example study validating some embodiments of systems,methods, and devices described herein.

FIG. 9N is a forest plot of hazard ratios of association with acutecoronary syndrome derived from an example study validating someembodiments of systems, methods, and devices described herein.

FIG. 9O illustrates acute coronary syndrome event-free survival rate inpatients without and with high-risk plaque morphology lesions in anexample study validating some embodiments of systems, methods, anddevices described herein.

FIG. 9P is a forest plot of hazard ratios of association withlow-density non-calcified plaque with acute coronary syndrome derivedfrom an example study validating some embodiments of systems, methods,and devices described herein.

FIG. 9Q illustrates acute coronary syndrome event-free survival rates inpatients stratified by the amount of low-density non-calcified plaquevolume derived from an example study validating some embodiments ofsystems, methods, and devices described herein.

FIG. 9R illustrates how high-risk plaque morphology can be considered toincrease risk for acute coronary syndrome in some embodiments.

FIG. 10A is a schematic illustrating an example embodiment(s) ofsystems, methods, and devices for cardiovascular risk and/or diseasestate assessment using image-based analysis of vessel surface and/orcoordinates.

FIG. 10B is a schematic illustrating an example of one or more regionsof plaque that are closer to the myocardium versus epicardium.

FIG. 10C is a flowchart illustrating an example embodiment(s) ofsystems, methods, and devices for cardiovascular risk and/or diseasestate assessment using image-based analysis of vessel surface and/orcoordinates.

FIG. 10D is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods described herein.

FIG. 11A is a schematic illustrating an example of one or more regionsof calcified and/or non-calcified plaque that can be analyzed usingimage analysis processes by one or more embodiments of the systems,methods, and devices herein for assessment of cardiovascular risk,disease, and/or state.

FIG. 11B is a flowchart illustrating an example embodiment of a system,device, and method for cardiovascular risk and/or disease stateassessment using image-based analyses of non-calcified and/or calcifiedplaque.

FIG. 11C is a flowchart illustrating another example embodiment of asystem, device, and method for cardiovascular risk and/or disease stateassessment using image-based analyses of non-calcified and/or calcifiedplaque.

FIG. 11D is a flowchart illustrating example an embodiment of a system,device, and method for cardiovascular risk and/or disease stateassessment using image-based analyses of non-calcified plaque.

FIG. 11E is a flowchart illustrating example an embodiment of a system,device, and method for cardiovascular risk and/or disease stateassessment using image-based analyses of non-calcified plaque.

FIG. 11F is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods described herein.

FIG. 12A is a schematic illustrating an example of one or more regionsof plaque that can be analyzed using image analysis processes by one ormore embodiments of the systems, methods, and devices herein forassessment of cardiovascular risk, disease, and/or state.

FIGS. 12B-12C are flowcharts illustrating example embodiments ofsystems, devices, and methods for cardiovascular risk and/or diseasestate assessment using modified and/or normalized image analysis-basedplaque parameters.

FIG. 12D is a flowchart illustrating example embodiments of systems,devices, and methods for developing a reference values database forcardiovascular risk and/or disease state assessment using modifiedand/or normalized image analysis-based plaque parameters.

FIG. 12E is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods described herein.

FIG. 13A is a schematic illustrating an example of an embodiment(s) ofan immersive patient-specific report on cardiovascular disease risk,state, diagnosis, and/or treatment that can be generated using one ormore embodiments of the systems, methods, and devices described herein.

FIG. 13B is a schematic illustrating an example of an embodiment(s) ofan immersive patient-specific report on cardiovascular disease risk,state, diagnosis, and/or treatment that can be generated using one ormore embodiments of the systems, methods, and devices described herein.

FIG. 13C is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for generation of an immersivepatient-specific report on cardiovascular disease risk, state,diagnosis, and/or treatment.

FIG. 13D is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods described herein.

FIG. 14A is a schematic illustrating an example of one or more regionsof plaque that can be analyzed using image analysis processes by one ormore embodiments of the systems, methods, and devices herein forassessment of cardiovascular risk, disease, and/or state.

FIGS. 14B-14C are schematics illustrating an example of one or moreregions of plaque that can be analyzed using image analysis processes byone or more embodiments of the systems, methods, and devices herein forassessment of cardiovascular risk, disease, and/or state.

FIGS. 14D-14G are flowcharts illustrating example embodiment(s) ofsystems, devices, and methods for cardiovascular risk and/or diseasestate assessment using modified and/or normalized image analysis-basedplaque parameters.

FIG. 14H is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods described herein.

FIG. 15A is a schematic illustrating an example embodiment(s) ofsystems, devices, and methods for non-invasive image-based determinationof fractional flow reserve (FFR) and/or ischemia.

FIGS. 15B-15C are schematics illustrating results of a study utilizingan example embodiment(s) of systems, devices, and methods fornon-invasive image-based determination of fractional flow reserve (FFR)and/or ischemia.

FIG. 15D is a schematic illustrating an example embodiment(s) ofsystems, devices, and methods for non-invasive image-based determinationof fractional flow reserve (FFR) and/or ischemia.

FIG. 15E is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for non-invasive image-based determinationof fractional flow reserve (FFR) and/or ischemia.

FIG. 15F is a block diagram depicting an embodiment(s) of a computerhardware system configured to run software for implementing one or moreembodiments of systems, devices, and methods described herein.

DETAILED DESCRIPTION

Although several embodiments, examples, and illustrations are disclosedbelow, it will be understood by those of ordinary skill in the art thatthe inventions described herein extend beyond the specifically disclosedembodiments, examples, and illustrations and includes other uses of theinventions and obvious modifications and equivalents thereof.Embodiments of the inventions are described with reference to theaccompanying figures, wherein like numerals refer to like elementsthroughout. The terminology used in the description presented herein isnot intended to be interpreted in any limited or restrictive mannersimply because it is being used in conjunction with a detaileddescription of certain specific embodiments of the inventions. Inaddition, embodiments of the inventions can comprise several novelfeatures and no single feature is solely responsible for its desirableattributes or is essential to practicing the inventions hereindescribed.

Disclosed herein are systems, devices, and methods for non-invasiveimage-based plaque analysis and risk determination. In particular, insome embodiments, the systems, devices, and methods described herein arerelated to analysis of one or more regions of plaque, such as forexample coronary plaque, based on one or more distances, volumes,shapes, morphologies, embeddedness, and/or axes (or dimension)measurements. “Plaque” or “a region of plaque” or “one or more regionsof plaque” may be referred to simply as “plaque” for ease of referenceunless otherwise indicated, explicitly or by context. For example, insome embodiments, the systems, devices, and methods described herein arerelated to plaque analysis based on one or more of distance betweenplaque and a vessel wall, distance between plaque and a lumen wall,length along longitudinal axis of plaque, length along latitudinal axisof plaque, volume of low density non-calcified plaque, volume of totalplaque, a ratio(s) between volume of low density non-calcified plaqueand volume of total plaque, embeddedness of low density non-calcifiedplaque, and/or the like. In some embodiments, the systems, devices, andmethods described herein are configured to determine a risk of coronaryartery disease (CAD), such as for example myocardial infarction (MI),based on one or more plaque analyses described herein. In someembodiments, the systems, devices, and methods described herein areconfigured to generate a proposed treatment and/or graphicalrepresentation based on the determined risk of CAD and/or one or moreplaque analyses described herein.

Also disclosed herein are systems, methods, and devices forcardiovascular risk and/or state assessment using image-based analyses.In particular, in some embodiments, the systems, devices, and methodsare related to cardiovascular risk and/or disease state assessment usingimage-based analysis of vessel surface and/or coordinates of features.In some embodiments, assessment of cardiovascular risk and/or diseasestate generated using the systems, methods, and devices herein can beutilized to diagnose and/or generate a proposed treatment for a patient.

Also disclosed herein are systems, methods, and devices forcardiovascular risk and/or state assessment using image-based analyses,where in some embodiments, the systems, devices, and methods are relatedto cardiovascular risk and/or disease state assessment using image-basedanalysis of vessel surface and/or coordinates of features. In someembodiments, assessment of cardiovascular risk and/or disease stategenerated using the systems, methods, and devices herein can be utilizedto diagnose and/or generate a proposed treatment for a patient.

Also disclosed herein are systems, methods, and devices forcardiovascular risk and/or state assessment using image-based analyses,where in some embodiments the systems, devices, and methods are relatedto cardiovascular risk and/or disease and/or state assessment usingmodified and/or normalized image analysis-based plaque parameters. Insome embodiments, assessment of cardiovascular risk and/or diseaseand/or state generated using the systems, methods, and devices hereincan be utilized to diagnose and/or generate a proposed treatment for apatient.

Disclosed herein are systems, methods, and devices for generation of apatient-specific report on the risk and/or state assessment, diagnosis,and/or treatment of cardiovascular disease, including for examplecoronary artery disease (CAD). In particular, in some embodiments, thesystems, devices, and methods are configured to generate an immersivepatient-specific report on the patient's cardiovascular disease risk,state, diagnosis, and/or treatment. In some embodiments, the systems,devices, and methods are configured to generate an immersivepatient-specific report based at least in part on image-based analysis,for example of one or more plaque and/or vessel parameters. In someembodiments, the systems, devices, and methods are configured to viewthe patient's cardiovascular disease state or risk from a point of viewwithin one or more arteries of the patient. In some embodiments, thesystems, devices, and methods are configured to graphically view and/ortrack actual or hypothetical progression of the patient's cardiovasculardisease state or risk based on actual or proposed treatment from a pointof view within one or more arteries of the patient.

Disclosed herein are systems, methods, and devices for cardiovascularrisk and/or state assessment using image-based analyses, wherein in someembodiments the systems, devices, and methods are related tocardiovascular risk and/or disease and/or state assessment usingnormalized image analysis-based plaque parameters. In some embodiments,assessment of cardiovascular risk and/or disease and/or state generatedusing the systems, methods, and devices herein can be utilized todiagnose and/or generate a proposed treatment for a patient.

Disclosed herein are systems, devices, and methods for non-invasiveimage-based determination of fractional flow reserve (FFR) and/orischemia. In particular, in some embodiments, the systems, devices, andmethods are related to FFR and/or ischemia analysis of arteries, such ascoronary, aortic, and/or carotid arteries using one or more imageanalysis techniques. For example, in some embodiments, the systems,methods, and devices can be configured to derive one or more stenosisand/or normal measurements from a medical image, which can be obtainednon-invasively, and use the same to derive an assessment of FFR and/orischemia. In some embodiments, the systems, methods, and devices can beconfigured to apply one or more allometric scaling laws to one or morestenosis and/or normal measurements to derive and/or generate anassessment of FFR and/or ischemia.

Coronary heart disease affects over 17.6 million Americans. The currenttrend in treating cardiovascular health issues is generally two-fold.First, physicians generally review a patient's cardiovascular healthfrom a macro level, for example, by analyzing the biochemistry or bloodcontent or biomarkers of a patient to determine whether there are highlevels of cholesterol elements in the bloodstream of a patient. Inresponse to high levels of cholesterol, some physicians will prescribeone or more drugs, such as statins, as part of a treatment plan in orderto decrease what is perceived as high levels of cholesterol elements inthe bloodstream of the patient.

The second general trend for currently treating cardiovascular healthissues involves physicians evaluating a patient's cardiovascular healththrough the use of angiography to identify large blockages in variousarteries of a patient. In response to finding large blockages in variousarteries, physicians in some cases will perform an angioplasty procedurewherein a balloon catheter is guided to the point of narrowing in thevessel. After properly positioned, the balloon is inflated to compressor flatten the plaque or fatty matter into the artery wall and/or tostretch the artery open to increase the flow of blood through the vesseland/or to the heart. In some cases, the balloon is used to position andexpand a stent within the vessel to compress the plaque and/or maintainthe opening of the vessel to allow more blood to flow. About 500,000heart stent procedures are performed each year in the United States.

However, a recent federally funded S100 million study calls intoquestion whether the current trends in treating cardiovascular diseaseare the most effective treatment for all types of patients. The recentstudy involved over 5,000 patients with moderate to severe stable heartdisease from 320 sites in 37 countries and provided new evidence showingthat stents and bypass surgical procedures are likely no more effectivethan drugs combined with lifestyle changes for people with stable heartdisease. Accordingly, it may be more advantageous for patients withstable heart disease to forgo invasive surgical procedures, such asangioplasty and/or heart bypass, and instead be prescribed heartmedicines, such as statins, and certain lifestyle changes, such asregular exercise. This new treatment regimen could affect thousands ofpatients worldwide. Of the estimated 500,000 heart stent proceduresperformed annually in the United States, it is estimated that a fifth ofthose are for people with stable heart disease. It is further estimatedthat 25% of the estimated 100,000 people with stable heart disease, orroughly 23,000 people, are individuals that do not experience any chestpain. Accordingly, over 20,000 patients annually could potentially forgoinvasive surgical procedures or the complications resulting from suchprocedures.

To determine whether a patient should forego invasive surgicalprocedures and opt instead for a drug regimen and/or to generate a moreeffective treatment plan, it can be important to more fully understandthe cardiovascular disease of a patient. Specifically, it can beadvantageous to better understand the arterial vessel health of apatient. For example, it is helpful to understand whether plaquebuild-up in a patient is mostly fatty matter build-up or mostlycalcified matter build-up, because the former situation may warranttreatment with heart medicines, such as statins, whereas in the lattersituation a patient should be subject to further periodic monitoringwithout prescribing heart medicine or implanting any stents. However, ifthe plaque build-up is significant enough to cause severe stenosis ornarrowing of the arterial vessel such that blood flow to heart musclemight be blocked, then an invasive angioplasty procedure to implant astent may likely be required because heart attack or sudden cardiacdeath (SCD) could occur in such patients without the implantation of astent to enlarge the vessel opening. Sudden cardiac death is one of thelargest causes of natural death in the United States, accounting forapproximately 325,000 adult deaths per year and responsible for nearlyhalf of all deaths from cardiovascular disease. For males, SCD is twiceas common as compared to females. In general, SCD strikes people in themid-30 to mid-40 age range. In over 50% of cases, sudden cardiac arrestoccurs with no warning signs.

With respect to the millions suffering from heart disease, there is aneed to better understand the overall health of the artery vesselswithin a patient beyond just knowing the blood chemistry or content ofthe blood flowing through such artery vessels. For example, in someembodiments of systems, devices, and methods disclosed herein, arterieswith “good” or stable plaque or plaque comprising hardened calcifiedcontent are considered non-life threatening to patients whereas arteriescontaining “bad” or unstable plaque or plaque comprising fatty materialare considered more life threatening because such bad plaque may rupturewithin arteries thereby releasing such fatty material into the arteries.Such a fatty material release in the blood stream can cause inflammationthat may result in a blood clot. A blood clot within an artery canprevent blood from traveling to heart muscle thereby causing a heartattack or other cardiac event. Further, in some instances, it isgenerally more difficult for blood to flow through fatty plaque buildupthan it is for blood to flow through calcified plaque build-up.Therefore, there is a need for better understanding and analysis of thearterial vessel walls of a patient.

Further, while blood tests and drug treatment regimens are helpful inreducing cardiovascular health issues and mitigating againstcardiovascular events (for example, heart attacks), such treatmentmethodologies are not complete or perfect in that such treatments canmisidentify and/or fail to pinpoint or diagnose significantcardiovascular risk areas. For example, the mere analysis of the bloodchemistry of a patient will not likely identify that a patient hasartery vessels having significant amounts of fatty deposit material badplaque buildup along a vessel wall. Similarly, an angiogram, whilehelpful in identifying areas of stenosis or vessel narrowing, may not beable to clearly identify areas of the artery vessel wall where there issignificant buildup of bad plaque. Such areas of buildup of bad plaquewithin an artery vessel wall can be indicators of a patient at high riskof suffering a cardiovascular event, such as a heart attack. In certaincircumstances, areas where there exist areas of bad plaque can lead to arupture wherein there is a release of the fatty materials into thebloodstream of the artery, which in turn can cause a clot to develop inthe artery. A blood clot in the artery can cause a stoppage of bloodflow to the heart tissue, which can result in a heart attack.Accordingly, there is a need for new technology for analyzing arteryvessel walls and/or identifying areas within artery vessel walls thatcomprise a buildup of plaque whether it be bad or otherwise.

In some embodiments, the systems, devices, and methods described hereinare configured to utilize non-invasive medical imaging technologies,such as a CT image or CCTA for example, which can be inputted into acomputer system configured to automatically and/or dynamically analyzethe medical image to identify one or more coronary arteries and/orplaque within the same. For example, in some embodiments, the system canbe configured to utilize one or more machine learning and/or artificialintelligence algorithms to automatically and/or dynamically analyze amedical image to identify, quantify, and/or classify one or morecoronary arteries and/or plaque. In some embodiments, the system can befurther configured to utilize the identified, quantified, and/orclassified one or more coronary arteries and/or plaque to generate atreatment plan, track disease progression, and/or a patient-specificmedical report, for example using one or more artificial intelligenceand/or machine learning algorithms In some embodiments, the system canbe further configured to dynamically and/or automatically generate avisualization of the identified, quantified, and/or classified one ormore coronary arteries and/or plaque, for example in the form of agraphical user interface. Further, in some embodiments, to calibratemedical images obtained from different medical imaging scanners and/ordifferent scan parameters or environments, the system can be configuredto utilize a normalization device comprising one or more compartments ofone or more materials.

As will be discussed in further detail, the systems, devices, andmethods described herein allow for automatic and/or dynamic quantifiedanalysis of various parameters relating to plaque, cardiovasculararteries, and/or other structures. More specifically, in someembodiments described herein, a medical image of a patient, such as acoronary CT image or CCTA, can be taken at a medical facility. Ratherthan having a physician eyeball or make a general assessment of thepatient, the medical image is transmitted to a backend main server insome embodiments that is configured to conduct one or more analysesthereof in a reproducible manner. As such, in some embodiments, thesystems, methods, and devices described herein can provide a quantifiedmeasurement of one or more features of a coronary CT image usingautomated and/or dynamic processes. For example, in some embodiments,the main server system can be configured to identify one or morevessels, plaque, fat, and/or one or more measurements thereof from amedical image. Based on the identified features, in some embodiments,the system can be configured to generate one or more quantifiedmeasurements from a raw medical image, such as for example radiodensityof one or more regions of plaque, identification of stable plaque and/orunstable plaque, volumes thereof, surface areas thereof, geometricshapes, heterogeneity thereof, and/or the like. In some embodiments, thesystem can also generate one or more quantified measurements of vesselsfrom the raw medical image, such as for example diameter, volume,morphology, and/or the like. Based on the identified features and/orquantified measurements, in some embodiments, the system can beconfigured to generate a risk and/or disease state assessment and/ortrack the progression of a plaque-based disease or condition, such asfor example atherosclerosis, stenosis, and/or ischemia, using rawmedical images. Further, in some embodiments, the system can beconfigured to generate a visualization of GUI of one or more identifiedfeatures and/or quantified measurements, such as a quantized colormapping of different features. In some embodiments, the systems,devices, and methods described herein are configured to utilize medicalimage-based processing to assess for a subject his or her risk of acardiovascular event, major adverse cardiovascular event (MACE), rapidplaque progression, and/or non-response to medication. In particular, insome embodiments, the system can be configured to automatically and/ordynamically assess such health risk of a subject by analyzing onlynon-invasively obtained medical images. In some embodiments, one or moreof the processes can be automated using an artificial intelligence (AI)and/or machine learning (ML) algorithm. In some embodiments, one or moreof the processes described herein can be performed within minutes in areproducible manner. This is stark contrast to existing measures todaywhich do not produce reproducible prognosis or assessment, takeextensive amounts of time, and/or require invasive procedures. In someembodiments, the systems, methods, and devices described herein compriseand/or are configured to utilize any one or more of such techniquesdescribed in US Patent Application Publication No. US 2021/0319558,which is incorporated herein by reference in its entirety.

As such, in some embodiments, the systems, devices, and methodsdescribed herein are able to provide physicians and/or patients specificquantified and/or measured data relating to a patient's plaque and/orischemia that do not exist today. In some embodiments, such detailedlevel of quantified plaque parameters from image processing anddownstream analytical results can provide more accurate and useful toolsfor assessing the health and/or risk of patients in completely novelways.

Disclosed are methods for identification of high-risk plaques usingvolumetric characterization of coronary plaque and perivascular adiposetissue data by computed tomography (CT) scanning. The volumetriccharacterization of the coronary plaque and perivascular adipose tissueallows for determination of the inflammatory status of the plaque by CTscanning. This is of use in the diagnosis, prognosis and treatment ofcoronary artery disease. While certain example embodiments are shown byway of example in the drawings and will herein be described in detail,these embodiments are capable of various modifications and alternativeforms. There is no intent to limit example embodiments to the particularforms disclosed, but on the contrary, example embodiments are to coverall modifications, equivalents, and alternatives falling within thescope of example embodiments.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of example embodiments. Asused herein, the term “and/or” includes all combinations of one or moreof the associated listed items.

The terminology used herein is for the purpose of describing embodimentsonly and is not intended to be limiting of example embodiments. As usedherein, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises,”“comprising,” “includes” and/or “including” when used herein, specifythe presence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. In this specification, the term“and/or” picks out each individual item as well as all combinations ofthem.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which example embodiments belongs. Itwill be further understood that terms, such as those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andshould not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein. It should also be noted that in somealternative implementations, the functions/acts noted may occur out ofthe order noted in the figures. For example, two figures shown insuccession may in fact be executed concurrently or may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

In the drawings, the dimensions of layers and regions are exaggeratedfor clarity of illustration. It will also be understood that when alayer (or tissue) is referred to as being “on” another layer or tissue,it can be directly on the other layer or substrate, or interveninglayers may also be present. Further, it will be understood that when alayer is referred to as being “under” another layer, it can be directlyunder, and one or more intervening layers may also be present. Inaddition, it will also be understood that when a layer is referred to asbeing ‘between’ two layers, it can be the only layer between the twolayers, or one or more intervening layers may also be present. Likereference numerals refer to like elements throughout.

Overview of Example Processing System to Characterize Coronary Plaque

This disclosure includes methods and systems of using data generatedfrom images collected by scanning a patient's arteries to identifycoronary artery plaques that are at higher risk of causing future heartattack or acute coronary syndrome. In particular, the characteristics ofperivascular coronary fat, coronary plaque, and/or the coronary lumen,and the relationship of the characteristics of perivascular coronaryfat, coronary plaque, and/or the coronary lumen are discussed todetermine ways for identifying the coronary plaque that is moresusceptible to implication in future ACS, heart attack and death. Theimages used to generate the image data may be CT images, CCTA images, orimages generated using any applicable technology that can depict therelative densities of the coronary plaque, perivascular fat, andcoronary lumen. For example, CCTA images may be used to generatetwo-dimensional (2D) or volumetric (three-dimensional (3-D)) image data,and this image data may be analyzed to determine certain characteristicsthat are associated with the radiodensities of the coronary plaque,perivascular fat, and/or coronary lumen. In some implementations, theHounsfield scale is used to provide a measure of the radiodensity ofthese features. A Hounsfield unit, as is known, represents an arbitraryunit of x-ray attenuation used for CT scans. Each pixel (2D) or voxel(3D) of a feature in the image data may be assigned a radiodensity valueon the Hounsfield scale, and then these values characterizing thefeatures may be analyzed.

In various embodiments, processing of image information may include: (1)determining scan parameters (for example, mA (milliampere), kvP (peakkilovoltage)); (2) determining the scan image quality (e.g., noise,signal-to-noise ratio, contrast-to-noise ratio); (3) measuringscan-specific coronary artery lumen densities (e.g., from a point distalto a coronary artery wall to a point proximal to the coronary arterywall to distal to the coronary artery, and from a central location ofthe coronary artery to an outer location (e.g., outer relative to radialdistance from the coronary artery): (4) measuring scan-specific plaquedensities (e.g., from central to outer, abruptness of change within aplaque from high-to-low or low-to-high) as a function of their 3D shape;and (5) measuring scan-specific perivascular coronary fat densities(from close to the artery to far from the artery) as a function of its3D shape.

From these measurements, which are agnostic to any commonly knownfeatures of ischemia-causing atherosclerosis, we can determine severalcharacteristics, including but not limited to:

-   -   1. A ratio of lumen attenuation to plaque attenuation, wherein        the volumetric model of scan-specific attenuation density        gradients within the lumen adjusts for reduced luminal density        across plaque lesions that are more functionally significant in        terms of risk value    -   2. A ratio of plaque attenuation to fat attenuation, wherein        plaques with high radiodensities are considered to present a        lower risk, even within a subset of plaques considered        “calcified,” where there can be a gradation of densities (for        example, 130 to 4000 HU) and risk is considered to be reduced as        density increases.    -   3. A ratio of lumen attenuation/plaque attenuation/fat        attenuation    -   4. A ratio of #1-3 as a function of 3D shape of atherosclerosis,        which can include a 3D texture analysis of the plaque    -   5. The 3D volumetric shape and path of the lumen along with its        attenuation density from the beginning to the end of the lumen.    -   6. The totality of plaque and plaque types before and after any        given plaque to further inform its risk.    -   7. Determination of “higher plaque risks” by “subtracting”        calcified (high-density) plaques to obtain a better absolute        measure of high risk plaques (lower-density plaques). In other        words, this particular embodiment involves identifying calcified        plaque and excluding it from further analysis of plaque for the        purpose of identifying high risk plaques.        Other characteristics can also be determined.

The above listed characteristics/metrics, and others, can be analyzedtogether to assess the risk of the plaque being implicated in futureheart attack, ACS, ischemia or death. This can be done throughdevelopment and/or validation of a traditional risk score or throughmachine learning methods. Factors for analysis from the metrics, thatare likely to be associated with heart attack, ACS, ischemia or death,may include: (1) a ratio of [bright lumen:dark plaque]; (2) a ratio of[dark plaque:light fat]; (3) a ratio of [bright lumen:dark plaque:lightfat]; and (4) a low ratio of [dark lumen:dark myocardium in 1 vesselarea]/[lumen:myocardium in another vessel area]. Some improvements inthe disclosed methods and systems include: (1) using numerical valuesfrom ratios of [lumen:plaque], [plaque:fat] and [lumen:plaque:fat]instead of using qualitative definitions of atherosclerotic features;(2) using a scan-specific [lumen:plaque attenuation] ratio tocharacterize plaque; (3) using a scan-specific [plaque:fat attenuation]ratio to characterize plaque; (4) using ratios of [lumen:plaque:fatcircumferential] to characterize plaque; and (5) integration of plaquevolume and type before and after as a contributor to risk for any givenindividual plaque.

Atherosclerotic plaque features may change over time with medicaltreatment (colchicine and statin medications) and while some of thesemedications may retard progression of plaque, they also have veryimportant roles in promoting the change in plaque. While statinmedications may have reduced the overall progression of plaque they mayalso have actually resulted in an increased progression of calcifiedplaque and a reduction of non-calcified plaque. This change will beassociated with a reduction in heart attack or ACS or death, and thedisclosed methods can be used to monitor the effects of medical therapyon plaque risk over time. Also, this method can also be used to identifyindividuals whose atherosclerotic plaque features or[lumen:plaque]/[plaque:fat]/[lumen:plaque:fat] ratios indicate that theyare susceptible to rapid progression or malignant transformation ofdisease. In addition, these methods can be applied to single plaques orto a patient-basis wherein whole-heart atherosclerosis tracking can beused to monitor risk to the patient for experiencing heart attack(rather than trying to identify any specific plaque as being causal forfuture heart attack). Tracking can be done by automated co-registrationprocesses of image data associated with a patient over a period of time.

FIG. 1 depicts a schematic of an example of an embodiment of a system100 that includes a processing system 120 configured to characterizecoronary plaque. The processing system 120 include one or more servers(or computers) 105 each configured with one or more processors. Theprocessing system 120 includes non-transitory computer memory componentsfor storing data and non-transitory computer memory components forstoring instructions that are executed by the one or more processorsdata communication interfaces, the instructions configuring the one ormore processors to perform methods of analyzing image information. Amore detailed example of a server/computer 105 is described in referenceto FIG. 9G.

The system 100 also includes a network. The processing system 120 is incommunication with the network 125. The network 125 may include, as atleast a portion of the network 125, the Internet, a wide area network(WAN), a wireless network, or the like. In some embodiments, theprocessing system 120 is part of a “cloud” implementation, which can belocated anywhere that is in communication with the network 125. In someembodiments, the processing system 120 is located in the same geographicproximity as an imaging facility that images and stores patient imagedata. In other embodiments, the processing system 120 is locatedremotely from where the patient image data is generated or stored.

FIG. 1 also illustrates in system 100 various computer systems anddevices 130 (e.g., of an imaging facility) that are related togenerating patient image data and that are also connected to the network125. One or more of the devices 130 may be at an imaging facility thatgenerates images of a patient's arteries, a medical facility (e.g., ahospital, doctor's office, etc.) or may be the personal computing deviceof a patient or care provider. For example, as illustrated in FIG. 1 ,an imaging facility server (or computer) 130A may be connected to thenetwork 125. Also, in this example, a scanner 130B in an imagingfacility maybe connected to the network 125. One or more other computerdevices may also be connected to the network 125. For example, a laptop130C, a personal computer 130D, and/or and an image information storagesystem 130E may also be connected to the network 125, and communicatewith the processing system 120, and each other, via the network 125.

In some examples, the scanner 130B can be a computed tomography (CT)scanner that uses a rotating X-ray tube and a row of detectors tomeasure X-ray attenuations by different tissues in the body and form acorresponding image. In another example, a scanner 130B can use aspinning tube (“spiral CT”) in which an entire X-ray tube and detectorsare spun around a central axis of the area being scanned. In anotherexample, the scanner 130B can utilize electron beam tomography (EBT). Inanother example, the scanner 130B can be a dual source CT scanner with atwo X-ray tube system. The methods and systems described herein can alsouse images from other CT scanners. In some examples, the scanner 130B isa photon counting CT scanner, a spectral CT scanner, or a dual energy CTscanner. A photon counting CT scanner, a spectral CT scanner, or a dualenergy CT scanner can help provide more detailed higher resolutionimages that better show small blood vessels, plaque, and other vascularpathologies, and allow for the determination of absolute materialdensities over relative densities. In general, a photon counting CTscanner uses an X-ray detector to count photons and quantifies theenergy, determining the count of the number of photons in severaldiscrete energy bins., resulting in higher contrast-to-noise ratio, andimproved spatial resolution and spectral imaging compared toconventional CT scanners. Each registered photon is assigned to aspecific bin depending on its energy, such that each pixel measures ahistogram of the incident X-ray spectrum. This spectral informationprovides several advantages, First, it can be used to quantitativelydetermine the material composition of each pixel in the reconstructed CTimage, as opposed to the estimated average linear attenuationcoefficient obtained in a conventional CT scan. The spectral/energyinformation can be used to remove beam hardening artifacts that occurhigher linear attenuation of many materials that shifts mean energy ofthe X-ray spectrum towards higher energies. Also, use of more than twoenergy bins allows discrimination between objects (bone, calcifications,contrast agents, tissue, etc.). In some embodiments, images generatedusing a photon counting CT scanner allows assessment of plaques atdifferent monochromatic energies as well as different polychromaticspectra (e.g., 100 kvp, 120 kvp, 140 kvp, etc.), and this can changedefinition of non-calcified and calcified plaques compared toconventional CT scanners. A spectral CT scanner uses different X-raywavelengths (or energies) to produce a CT scan. A dual energy CT scanneruses separate X-ray energies to detect two different energy ranges. Inan example, a dual energy CT scanner (also known as spectral CT) can usean X-ray detector with separate layers to detect two different energyranges (‘dual layer’). In another example, a dual energy CT scanner canuse a single scanner to scan twice using two different energy levels(e.g., electronic kVp switching). Images can be formed from combiningthe images detected at each different energy level, or the images may beused separately to assess a medical condition of a patient. In additionto providing absolute material densities, a photon counting CT scanneralso allows for evaluation of images that are “monochromatic” as opposedto the typical CT, which is polychromatic spectra of light. As notedabove, features (e.g., low density non-calcified plaque, calcifiedplaque, non-calcified plaque) that are depicted images formed using aphoton counting CT scanner, a spectral CT scanner, or a dual energy CTscanner may have different radiodensities than those depicted in imagesformed from a conventional CT scanner, that is, such images may affector change the definition of calcified and non-calcified plaque. However,radiodensities of calcified and non-calcified plaque, or other featuresdepicted in images formed from a photon counting CT scanner, a spectralCT scanner, or a dual energy CT scanner, can be normalized to correspondto densities of conventional CT scanners and to the densities disclosedherein. Accordingly, the radiodensities disclosed herein can be directlycorrelated to radiodensities of images generated with a photon countingCT scanner, a spectral CT scanner, or a dual energy CT scanner such thatthe systems and methods, analysis, plaque densities etc. disclosedherein are directly applicable to images formed from a photon countingCT scanner, a spectral CT scanner, or a dual energy CT scanner, and aredirectly applicable to images formed from a photon counting CT scanner,a spectral CT scanner, or a dual energy CT scanner that are normalizedto equivalent conventional CT scanner radiodensities.

The information communicated from the devices 130 to the processingsystem 120 via the network 125 may include image information 135. Invarious embodiments, the image information 135 may include 2D or 3Dimage data of a patient, scan information related to the image data,patient information, and other imagery or image related information thatrelates to a patient. For example, the image information may includepatient information including (one or more) characteristics of apatient, for example, age, gender, body mass index (BMI), medication,blood pressure, heart rate, height, weight, race, whether the patient isa smoker or non-smoker, body habitus (for example, the “physique” or“body type” which may be based on a wide range of factors), medicalhistory, diabetes, hypertension, prior coronary artery disease (CAD),dietary habits, drug history, family history of disease, informationrelating to other previously collected image information, exercisehabits, drinking habits, lifestyle information, lab results and thelike. In some embodiments, the image information includes identificationinformation of the patient, for example, patient's name, patient'saddress, driver's license number, Social Security number, or indicia ofanother patient identification. Once the processing system 120 analyzesthe image information 135, information relating to a patient 140 may becommunicated from the processing system 120 to a device 130 via thenetwork 125. The patient information 140 may include for example, apatient report. Also, the patient information 140 may include a varietyof patient information which is available from a patient portal, whichmay be accessed by one of the devices 130.

In some embodiments, image information comprising a plurality of imagesof a patient's coronary arteries and patient information/characteristicsmay be provided from one or more of the devices 130 to the one or moreservers 105 of the processing system 120 via a network 125. Theprocessing system 120 is configured to generate coronary arteryinformation using the plurality of images of the patient's coronaryarteries to generate two-dimensional and/or three-dimensional datarepresentations of the patient's coronary arteries. Then, the processingsystem 120 analyzes the data representations to generate patient reportsdocumenting a patient's health conditions and risks related to coronaryplaque. The patient reports may include images and graphical depictionsof the patient's arteries in the types of coronary plaque in or near thecoronary arteries. Using machine learning techniques or other artificialintelligent techniques, the data representations of the patient'scoronary arteries may be compared to other patients' datarepresentations (e.g., that are stored in a database) to determineadditional information about the patient's health. For example, based oncertain plaque conditions of the patient's coronary arteries, thelikelihood of a patient having a heart attack or other adverse coronaryeffect can be determined. Also, for example, additional informationabout the patient's risk of CAD may also be determined.

FIG. 2 is a schematic illustrating an example of a heart muscle 225 andits coronary arteries. The coronary vasculature includes a complexnetwork of vessels ranging from large arteries to arterioles,capillaries, venules, veins, etc. FIG. 1 depicts a model 220 of aportion of the coronary vasculature that circulates blood to and withinthe heart and includes an aorta 240 that supplies blood to a pluralityof coronary arteries, for example, a left anterior descending (LAD)artery 215, a left circumflex (LCX) artery 220, and a right coronary(RCA) artery 230, described further below. Coronary arteries supplyblood to the heart muscle 225. Like all other tissues in the body, theheart muscle 225 needs oxygen-rich blood to function. Also,oxygen-depleted blood must be carried away. The coronary arteries wraparound the outside of the heart muscle 225. Small branches dive into theheart muscle 225 to bring it blood. The examples of methods and systemsdescribed herein may be used to determine information relating to bloodflowing through the coronary arteries in any vessels extendingtherefrom. In particular, the described examples of methods and systemsmay be used to determine various information relating to one or moreportions of a coronary artery where plaque has formed which is then usedto determine risks associated with such plaque, for example, whether aplaque formation is a risk to cause an adverse event to a patient.

The right side 230 of the heart 225 is depicted on the left side of FIG.2 (relative to the page) and the left side 235 of the heart is depictedon the right side of FIG. 2 . The coronary arteries include the rightcoronary artery (RCA) 205 which extends from the aorta 240 downwardalong the right side 230 of the heart 225, and the left main coronaryartery (LMCA) 210 which extends from the aorta 240 downward on the leftside 235 of the heart 225. The RCA 205 supplies blood to the rightventricle, the right atrium, and the SA (sinoatrial) and AV(atrioventricular) nodes, which regulate the heart rhythm. The RCA 205divides into smaller branches, including the right posterior descendingartery and the acute marginal artery. Together with the left anteriordescending artery 215, the RCA 205 helps supply blood to the middle orseptum of the heart.

The LMCA 210 branches into two arteries, the anterior interventricularbranch of the left coronary artery, also known as the left anteriordescending (LAD) artery 215 and the circumflex branch of the leftcoronary artery 220. The LAD artery 215 supplies blood to the front ofthe left side of the heart. Occlusion of the LAD artery 215 is oftencalled the widow-maker infarction. The circumflex branch of the leftcoronary artery 220 encircles the heart muscle. The circumflex branch ofthe left coronary artery 220 supplies blood to the outer side and backof the heart, following the left part of the coronary sulcus, runningfirst to the left and then to the right, reaching nearly as far as theposterior longitudinal sulcus.

FIG. 3 illustrates an example of a set of images generated from scanningalong a coronary artery, including a selected image of a portion of acoronary artery, and how image data may correspond to a value on theHounsfield Scale. As discussed in reference to FIG. 1 , in addition toobtaining image data, scan information including metrics related to theimage data, and patient information including characteristics of thepatient may also be collected.

A portion of a heart 225, the LMCA 210, and the LAD artery 215 isillustrated in the example of FIG. 3 . A set of images 305 can becollected along portions of the LMCA 210 and the LAD artery 215, in thisexample from a first point 301 on the LMCA 210 to a second point 302 onthe LAD artery 215. In some examples, the image data may be obtainedusing noninvasive imaging methods. For example, CCTA image data can begenerated using a scanner to create images of the heart in the coronaryarteries and other vessels extending therefrom. Collected CCTA imagedata may be subsequently used to generate three-dimensional image modelsof the features contained in the CCTA image data (for example, the rightcoronary artery 205, the left main coronary artery 210, the leftanterior descending artery 215, the circumflex branch of the leftcoronary artery 220, the aorta 240, and other vessels related to theheart that appear in the image data.

In various embodiments, different imaging methods may be used to collectthe image data. For example, ultrasound or magnetic resonance imaging(MRI) may be used. In some embodiments, the imaging methods involveusing a contrast agent to help identify structures of the coronaryarteries, the contrast agent being injected into the patient prior tothe imaging procedure. The various imaging methods may each have theirown advantages and disadvantages of usage, including resolution andsuitability of imaging the coronary arteries. Imaging methods which maybe used to collect image data of the coronary arteries are constantlyimproving as improvements to the hardware (e.g., sensors and emitters)and software are made. The disclosed systems and methods contemplateusing CCTA image data and/or any other type of image data that canprovide or be converted into a representative 3D depiction of thecoronary arteries, plaque contained within the coronary arteries, andperivascular fat located in proximity to the coronary arteriescontaining the plaque such that attenuation or radiodensity values ofthe coronary arteries, plaque, and/or perivascular fat can be obtained.

Referring still to FIG. 3 , a particular image 310 of the image data 305is shown, which represents an image of a portion of the left anteriordescending artery 215. The image 310 includes image information, thesmallest point of the information manipulated by a system referred toherein generally as a pixel, for example pixel 315 of image 310. Theresolution of the imaging system used to capture the image data willaffect the size of the smallest feature that can be discerned in animage. In addition, subsequent manipulation of the image may affect thedimensions of a pixel. As one example, the image 310 in a digitalformat, may contain 4000 pixels in each horizontal row, and 3000 pixelsin each vertical column. Pixel 315, and each of the pixels in image data310 and in the image data 305, can be associated with a radiodensityvalue that corresponds to the density of the pixel in the image.Illustratively shown in FIG. 3 is mapping pixel 315 to a point on theHounsfield scale 320. The Hounsfield scale 320 is a quantitative scalefor describing radiodensity. The Hounsfield unit scale lineartransformation of the original linear attenuation coefficientmeasurement into one in which the radiodensity of distilled water atstandard pressure and temperature is defined as zero Hounsfield units(HU), while the radiodensity of air at standard pressure and temperatureis defined as −1000 HU. Although FIG. 3 illustrates an example ofmapping pixel 315 of image 310 to a point on the Hounsfield scale 320,such an association of a pixel to a radiodensity value can also be donewith 3D data. For example, after the image data 305 is used to generatea three-dimensional representation of the coronary arteries.

Once the data has been obtained and rendered into a three-dimensionalrepresentation, various processes can be performed on the data toidentify areas of analysis. For example, a three-dimensional depictionof a coronary artery may be segmented to define a plurality of portionsof the artery and identified as such in the data. In some embodiments,the data may be filtered (e.g., smoothed) by various methods to removeanomalies that are the result of scanning or other various errors.Various known methods for segmenting and smoothing the 3D data may beused, and therefore for brevity of the disclosure will not be discussedin any further detail herein.

FIG. 4A is a block diagram that illustrates a computer system 400 uponwhich various embodiments may be implemented. Computer system 400includes a bus 402 or other communication mechanism for communicatinginformation, and a hardware processor, or multiple processors, 404coupled with bus 402 for processing information. Hardware processor(s)404 may be, for example, one or more general purpose microprocessors.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM), cache and/or other dynamic storage devices, coupledto bus 402 for storing information and instructions to be executed byprocessor 404. Main memory 406 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 404. Such instructions, whenstored in storage media accessible to processor 404, render computersystem 400 into a special-purpose machine that is customized to performthe operations specified in the instructions. The main memory 406 may,for example, include instructions that analyze image information todetermine characteristics of coronary features (e.g., plaque,perivascular fat and coronary arteries) to produce patient reportscontaining information that characterizes aspects of the patient'shealth relating to their coronary arteries. For example, one or moremetrics may be determined, the metrics including one or more of aslope/gradient of a feature, a maximum density, minimum density, a ratioof a slope of one feature to the slope of another feature, a ratio of amaximum density of one feature to the maximum density of anotherfeature, a ratio of a minimum density of a feature to the minimumdensity of the same feature, or a ratio of the minimum density of afeature to the maximum density of another feature.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or USB thumb drive (Flash drive),etc., is provided and coupled to bus 402 for storing information andinstructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT) or LCD display (or touch screen), fordisplaying information to a computer user. An input device 414,including alphanumeric and other keys, is coupled to bus 402 forcommunicating information and command selections to processor 404.Another type of user input device is cursor control 416, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 404 and for controllingcursor movement on display 412. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Insome embodiments, the same direction information and command selectionsas cursor control may be implemented via receiving touches on a touchscreen without a cursor.

Computing system 400 may include a user interface module to implement aGUI that may be stored in a mass storage device as computer executableprogram instructions that are executed by the computing device(s).Computer system 400 may further, as described below, implement thetechniques described herein using customized hard-wired logic, one ormore ASICs or FPGAs, firmware and/or program logic which in combinationwith the computer system causes or programs computer system 400 to be aspecial-purpose machine. According to one embodiment, the techniquesherein are performed by computer system 400 in response to processor(s)404 executing one or more sequences of one or more computer readableprogram instructions contained in main memory 406. Such instructions maybe read into main memory 406 from another storage medium, such asstorage device 410. Execution of the sequences of instructions containedin main memory 406 causes processor(s) 404 to perform the process stepsdescribed herein. In alternative embodiments, hard-wired circuitry maybe used in place of or in combination with software instructions.

Various forms of computer readable storage media may be involved incarrying one or more sequences of one or more computer readable programinstructions to processor 404 for execution. For example, theinstructions may initially be carried on a magnetic disk or solid statedrive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN (or WAN component tocommunicate with a WAN). Wireless links may also be implemented. In anysuch implementation, communication interface 418 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through theworldwide packet data communication network now commonly referred to asthe “Internet” 428. Local network 422 and Internet 428 both useelectrical, electromagnetic or optical signals that carry digital datastreams. The signals through the various networks and the signals onnetwork link 420 and through communication interface 418, which carrythe digital data to and from computer system 400, are example forms oftransmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

Accordingly, in an embodiment, the computer system 105 comprises anon-transitory computer storage medium storage device 410 configured toat least store image information of patients. The computer system 105can also include non-transitory computer storage medium storage thatstores instructions for the one or more processors 404 to execute aprocess (e.g., a method) for characterization of coronary plaque tissuedata and perivascular tissue data using image data gathered from acomputed tomography (CT) scan along a blood vessel, the imageinformation including radiodensity values of coronary plaque andperivascular tissue located adjacent to the coronary plaque. Executingthe instructions, the one or more processors 404 can quantify, in theimage data, the radiodensity in regions of coronary plaque, quantify inthe image data, radiodensity in at least one region of correspondingperivascular tissue adjacent to the coronary plaque, determine gradientsof the quantified radiodensity values within the coronary plaque and thequantified radiodensity values within the corresponding perivasculartissue, determine a ratio of the quantified radiodensity values withinthe coronary plaque and the corresponding perivascular tissue, andcharacterizing the coronary plaque by analyzing one or more of thegradients of the quantified radiodensity values in the coronary plaqueand the corresponding perivascular tissue, or the ratio of the coronaryplaque radiodensity values and the radiodensity values of thecorresponding perivascular tissue.

Various embodiments of the present disclosure may be a system, a method,and/or a computer program product at any possible technical detail levelof integration. The computer program product may include a computerreadable storage medium (or mediums) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure. For example, the functionality described herein maybe performed as software instructions are executed by, and/or inresponse to software instructions being executed by, one or morehardware processors and/or any other suitable computing devices. Thesoftware instructions and/or other executable code may be read from acomputer readable storage medium (or mediums).

The computer readable storage medium can be a tangible device that canretain and store data and/or instructions for use by an instructionexecution device. The computer readable storage medium may be, forexample, but is not limited to, an electronic storage device (includingany volatile and/or non-volatile electronic storage devices), a magneticstorage device, an optical storage device, an electromagnetic storagedevice, a semiconductor storage device, or any suitable combination ofthe foregoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a solid state drive, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), a portable compact disc read-only memory (CD-ROM), a digitalversatile disk (DVD), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, and any suitable combination ofthe foregoing. A computer readable storage medium, as used herein, isnot to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions (as also referred to herein as,for example, “code,” “instructions,” “module,” “application,” “softwareapplication,” and/or the like) for carrying out operations of thepresent disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. Computer readable program instructions may be callable fromother instructions or from itself, and/or may be invoked in response todetected events or interrupts. Computer readable program instructionsconfigured for execution on computing devices may be provided on acomputer readable storage medium, and/or as a digital download (and maybe originally stored in a compressed or installable format that requiresinstallation, decompression or decryption prior to execution) that maythen be stored on a computer readable storage medium. Such computerreadable program instructions may be stored, partially or fully, on amemory device (e.g., a computer readable storage medium) of theexecuting computing device, for execution by the computing device. Thecomputer readable program instructions may execute entirely on a user'scomputer (e.g., the executing computing device), partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart(s) and/or block diagram(s)block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks. For example, the instructions may initially be carried on amagnetic disk or solid state drive of a remote computer. The remotecomputer may load the instructions and/or modules into its dynamicmemory and send the instructions over a telephone, cable, or opticalline using a modem. A modem local to a server computing system mayreceive the data on the telephone/cable/optical line and use a converterdevice including the appropriate circuitry to place the data on a bus.The bus may carry the data to a memory, from which a processor mayretrieve and execute the instructions. The instructions received by thememory may optionally be stored on a storage device (e.g., a solid statedrive) either before or after execution by the computer processor.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. In addition, certain blocks may be omitted insome implementations. The methods and processes described herein arealso not limited to any particular sequence, and the blocks or statesrelating thereto can be performed in other sequences that areappropriate.

FIG. 4B is a block diagram that illustrates examples of representativeinstructions which may be executed by one or more computer hardwareprocessors in one or more computer modules in a representativeprocessing system (computer system) 120 which may implement variousembodiments described herein. As illustrated in FIG. 1 , the processingsystem 120 can be implemented in one computer (for example, a server) orin 2 or more computers (two or more servers). Although the instructionsare represented in FIG. 4B as being in seven modules 450, 455, 460, 465,470, 475, 480, in various implementations the executable instructionsmay be in fewer modules, including a single module, or more modules.

The processing system 120 includes image information stored on a storagedevice 410, which may come from the network 125 illustrated in FIG. 1 .The image information may include image data, scan information, and/orpatient data. In this example, the storage device 410 also includesstored plaque information of other patients. For example, the storedplaque information of other patients may be stored in a database on thestorage device 410. In other examples, stored plaque information ofother patients is stored on a storage device that is in communicationwith processing system 120. The other patients' stored plaqueinformation may be a collection of information from one, dozens,hundreds, thousands, tens of thousands, hundreds of thousands, ormillions of patients, or more.

The information for each patient may include characterizations of thatpatient's plaque, such as densities and density gradients of thepatient's plaque, and the location of the plaque relative to theperivascular tissue near or adjacent to the plaque. The information foreach patient may include patient information. For example, theinformation may include one or more of sex, age, BMI (body mass index),medication, blood pressure, heart rate, weight, height, race, bodyhabitus, smoking history, history or diagnosis of diabetes, history ordiagnosis of hypertension, prior coronary artery disease, family historyof coronary artery disease and/or other diseases, or one or more labresults (e.g., blood test results). The information for each patient mayinclude scan information. For example, the information may include oneor more of contrast-to-noise ratio, signal-to-noise ratio, tube current,tube voltage, contrast type, contrast volume, flow rate, flow duration,slice thickness, slice spacing, pitch, vasodilator, beta blockers, reconoption whether it's iterative or filter back projection, recon typewhether it's standard or high resolution, display field-of-view,rotation speed, gating whether it's perspective triggering orretrospective gating, stents, heart rate, or blood pressure. Theinformation for each patient may also include cardiac information. Forexample, the information may include characterizations of plaqueincluding one or more of density, volume, geometry(shape), location,remodeling, baseline anatomy (for diameter, length), compartments(inner, outer, within), stenosis (diameter, area), myocardial mass,plaque volume, and/or plaque composition, texture, or uniformity.

The processing system 120 also includes memory 406, 408, which may bemain memory of the processing system or read only memory (ROM). Thememory 406, 408 stores instructions executable by one or more computerhardware processors 404 (groups of which referred to herein as“modules”) to characterize coronary plaque. The memory 406, 408 will becollectively referred to, in reference to this diagram, as memory 406for the sake of brevity. Examples of the functionality that is performedby the executable instructions are described below.

Memory 406 includes module 450 that generates, from the image datastored on the storage device 410, 2-D or 3-D representations of thecoronary arteries, including plaque, and perivascular tissue that islocated adjacent to or in proximity of the coronary arteries in theplaque. The generation of the 2-D or 3-D representations of the coronaryarteries may be done from a series of images 305 (e.g., CCTA images) isdescribed above in reference to FIG. 3 . Once the representation of thecoronary arteries are generated, different portions or segments of thecoronary arteries can be identified for evaluation. For example,portions of interest of the right coronary artery 205, the left anteriordescending artery 215, or the circumflex branch of the left coronaryartery 220 may be identified as areas of analysis (areas of interest)based on input from a user, or based on a feature determined from therepresentation of the coronary artery (plaque).

In module 460, the one or more computer hardware processors quantifyradiodensity in regions of coronary plaque. For example, theradiodensity in regions of coronary plaque are set to a value on theHounsfield scale. In module 465, the one or more computer hardwareprocessors quantify radiodensity of perivascular tissue that is adjacentto the coronary plaque, and quantify radiodensity value of the lumen ofthe vessel of interest. In module 470, the one or more computer hardwareprocessors determine gradients of the radiodensity values of the plaquethe perivascular tissue and/or the lumen. In module 475, the one or morecomputer hardware processors determine one or more ratios of theradiodensity values in the plaque, perivascular tissue, and/or thelumen. Next, in module 480, the one or more computer hardware processorscharacterize the coronary plaque using the gradients of the plaque, theperivascular tissue, and/or the lumen, and/or characterize ratio of theradiodensity values of the coronary plaque to perivascular tissue and/orthe lumen including comparing the gradients and or ratios to a databasecontaining information of other patients' plaque gradients and ratios.For example, the gradients and/or the ratios are compared to patientdata that stored on storage device 410. Determining gradients and ratiosof the plaque the perivascular tissue and the lumen are described inmore detail with reference to FIGS. 6-12 .

FIG. 5A illustrates an example of a flowchart of a process 500 foranalyzing coronary plaque. At block 505, the process 500 generates imageinformation including image data relating to coronary arteries. Invarious embodiments, this may be done by a scanner 130B (FIG. 1 ). Atblock 510, a processing system may receive image information via anetwork 125 (FIG. 1 ), the image information including the image data.At block 515, the process 500 generates a 3D representation of thecoronary arteries including perivascular fat and plaque on theprocessing system. The functionality of blocks 505, 510, and 515, can beperformed, for example, using various scanning techniques (e.g., CCTA)to generate image data, communication techniques to transfer data overthe network, and processing techniques to generate the 3D representationof the coronary arteries from the image data.

At block 520, the processing system performs a portion of the process500 to analyze the coronary plaque, which is described in further detailin reference to process 550 of FIG. 5B. Additional details of thisprocess to analyze the coronary plaque in reference to FIGS. 6-12 .

FIG. 5B illustrates an example of a flowchart that expands on a portionof the flowchart in FIG. 5A for determining characteristics of coronaryplaque. Referring now to FIG. 5B, at block 555, process 550 can utilizethe one or more processors 404 to quantify the radiodensity in regionsof coronary plaque. At block 560, the process 550 can utilize the one ormore processors 404 to quantify, in the image data, radiodensity in atleast one region of corresponding perivascular tissue, meaningperivascular tissue that is adjacent to the coronary plaque. At block565, the process 550 determines gradients of the quantified radiodensityvalues within the coronary plaque and the quantified radiodensity valueswithin the corresponding perivascular tissue. The one or more processors404 can be the means to determine these gradients. At block 570, theprocess 550 may determine a ratio of the quantified radiodensity valueswithin the coronary plaque and the corresponding perivascular tissue.For example, the perivascular tissue that is adjacent to the coronaryplaque. The one or more processors 404 can determine these ratios. Atblock 575, process 550 can utilize the one or more processors 404 tocharacterize the coronary plaque by analyzing one or more of thegradients of the quantified radiodensity values in the coronary plaqueand the corresponding perivascular tissue, or the ratio of the coronaryplaque radiodensity values and the radiodensity values of thecorresponding perivascular tissue. The process 550 can then return toprocess 500 as illustrated by the circle A.

Referring again to FIG. 5A, at block 525, the process 500 may comparedetermined information of a particular patient's coronary plaque tostored patient data, for example patient data stored on storage device410. An example of the coronary plaque information of a particularpatient that can be compared to stored patient data. To betterunderstand the patient's coronary plaque information, and/or to helpdetermine the particular patient's coronary plaque information, one ormore of the scan information may be used. Also, when comparing aparticular patient's coronary plaque information to previously storedcoronary plaque information, one or more characteristics of the patientmay be compared, including, for example, one or more of thecharacteristics of a patient. In some examples, the coronary plaqueinformation of the patient being examined may be compared to or analyzedin reference to a patient who has one or more of the same or similarpatient characteristics. For example, the patient being examined may becompared to a patient that has the same or similar characteristics ofsex, age, BMI, medication, blood pressure, heart rate, weight, height,race, body habitus, smoking, diabetes, hypertension, prior coronaryartery disease, family history, and lab results. Such comparisons can bedone through various means, for example machine learning and/orartificial intelligence techniques. In some examples, neural network isused to compare a patient's coronary artery information to numerous(e.g., 10,000+) other patients' coronary artery information. For suchpatients that have similar patient information and similar cardiacinformation, risk assessments of the plaque of the patient beingexamined may be determined.

FIG. 6 illustrates an example of an area, indicated by box 605, wherecontrast attenuation patterns in a proximal portion of the coronarylumen can be analyzed, box 605 extending from a central area of thevessel 665 towards the vessel wall 661. FIG. 6 illustrates anotherexample of an area, indicated by box 652, where contrast attenuationpatterns in a portion of the coronary lumen of vessel 665 can beanalyzed, box 652 extending longitudinally relative to vessel 665 from acentral area of the vessel 665 towards the vessel wall 661. FIG. 6further illustrates an example of an area, indicated by box 662, wherecontrast attenuation patterns of a portion of the lumen, a portion offibrous plaque 610 and plaque 620 can be analyzed, box 662 thus coveringa portion of the vessel 665 and a portion of fibrous plaque 610 andplaque 620. FIG. 6 further illustrates an example of an area indicatedby box 642, where contrast attenuation patterns of a portion of plaque635 and a portion of fat 640 positioned adjacent to plaque 635 can beanalyzed, box 642 extending over a portion of plaque 635 and a portionof fat 640. Information determined by analyzing various aspects of thedensity of coronary artery features (e.g., the lumen, the plaque, and/orthe perivascular fat) can be combined with other information todetermine characteristics of a patient's arteries. In some examples, thedetermined information may include for any of the lumen, plaque orperivascular fat, one or more of a slope/gradient of a feature, amaximum density, a minimum density, a ratio of a slope of the density ofone feature to the slope of the density of another feature, a ratio of amaximum density of one feature to the maximum density of anotherfeature, a ratio of a minimum density of a feature to the minimumdensity of the same feature, a directionality of the density ratios,e.g., a density ratio between features facing one way or direction andfeatures facing in an opposite direction (for example, the radiodensityratio of features facing inwards towards the myocardium and featuresfacing outwards toward the pericardium), or a ratio of the minimumdensity of a feature to the maximum density of another feature. Suchdetermined information may indicate distinct differences in risks ofplaque in a patient. In some examples, determined information (forexample as listed above) may be used with a percentage diameter ofstenosis to determine characteristics of a patient's arteries.

Still referring to FIG. 6 , in an example of the directionality ofradiodensity ratios, the density of a portion of the necrotic coreplaque 615 to the density of a portion of the vessel 665 (e.g.,plaque:vessel inward facing ratio) can be determined and may indicate acertain risk of plaque. In another example of the directionality ofradiodensity ratios, the density of a portion of a portion of the vessel665 to the density of the necrotic core plaque 615 (e.g., vessel:plaqueoutward facing) can be determined and may indicate a certain risk ofplaque. In another example, the density ratio of the necrotic coreplaque 615 to the density of a portion of the vessel 665 (e.g.,plaque:vessel inward facing ratio) can be compared to the density ratioof the necrotic core plaque 615 to the fibrous plaque 620 (e.g.,plaque:plaque outward facing) may indicate a certain risk of plaque. Inother examples, features that are adjacently positioned can be used todetermine inward and/or outward directional radiodensity values that maybe used to indicate a risk associated with plaque. Such ratios mayprovide distinct differences in risk of plaque. Various embodiments ofdirectional radiodensity values and/or directional radiodensity ratioscan be included with any of the other information described herein toindicates plaque risk.

The size of a compartment may be used to also indicate a risk associatedwith plaque. For example, determination of risk associated with a plaquemay be based at least partially on the size of the compartments, suchthat the ratio of the of the radiodensities affects the determination ofrisk and the function of the size of the compartments can also affectthe determination of risk. While the presence of plaque in a patientwhere the ratio of plaque:fat may indicate a high risk plaque, if thereis only a small amount of plaque (e.g., a small compartment of plaque),it would be of risk than if there was a larger compartment of the sameplaque with the same radiodensity ratio of plaque to fat. In oneimplementation, the size (e.g., a volume) of the compartment a feature(e.g., of lumen, plaque, perivascular tissue (fat), and myocardium) canbe determined, and a radiodensity ratio can also be determined, and thenthe ratio can be weighted based on the size of the compartment. Forexample, a large compartment can increase the weight of a ratio to makethe ratio more indicative of a risk associated with the plaque.Similarly, a small compartment can decrease the weight of a ratio tomake the ratio less indicative of a risk associated with the plaque. Inan implementation, only the compartment size of the plaque is used toweight (or adjust) the ratio. In an implementation, the compartment sizeof both of the features that are used in the radiodensity ratio can beused to weight the ratio to determine a resulting risk. In animplementation, the compartment size of one of plaque, lumen,perivascular tissue, or myocardium is used to weight (or adjust) therisk associated with the radiodensity ratio. In an implementation, thecompartment size of more than one of plaque, lumen, perivascular tissue,or myocardium is used to weight the risk associated with theradiodensity ratio. Various embodiments of determining plaque risk usingcompartment size can be included with any of the other informationdescribed herein to indicate plaque risk.

FIG. 7 illustrates the same vessel 665 and features of plaque and fat asillustrated in FIG. 6 and further illustrates additional examples ofareas of an artery, and plaque and/or perivascular fat near the artery,that may be analyzed to determine characteristics of a patient'sarteries. Such areas are indicated in FIG. 7 by rectangular boxes,similar to the illustrations in FIG. 6 . Although particular locationsof the rectangular boxes are illustrated in FIG. 6 and FIG. 7 , theseare only examples of areas that may be analyzed. In one example, FIG. 7illustrates box 660 which includes a portion of the vessel 665, aportion of necrotic core plaque 615, a portion of fibrous plaque 610, aportion of plaque 620, and a portion of fat 625. In another example,FIG. 7 illustrates box 655 which includes a portion of the vessel 665, aportion of the fibers plaque 610 a portion of the plaque 620 the portionof the necrotic core plaque 615, and a portion of fat 625. Box 655 may,in some cases, illustrate the general area for analysis due to theexistence of 3 different types of plaque 610, 615, 620, and adjacentlydisposed fat 625. Particular portions of a general area for analysis maybe analyzed to better understand the characteristics formed by adjacentfeatures. For example, FIG. 7 illustrates the general area 665containing box 660 (described above), box 673, which extends across aportion of fibrous plaque 610 and plaque 620, and box 674 which extendsacross a portion of plaque 620 and perivascular fat 625. As anotherexample, FIG. 7 also illustrates another box 672 that extends across aportion of the vessel 655 and necrotic core plaque 615. As a furtherexample, FIG. 7 illustrates box 671 that extends across a portion of thevessel 665 and fat 640 juxtaposed to the vessel 665. As a furtherexample, FIG. 7 illustrates box 670 that extends across a portion of thevessel 665 and plaque 635. As indicated above, characteristics of apatient's arteries that can be analyzed based on these features caninclude but are not limited to:

-   -   1. A ratio of lumen attenuation to plaque attenuation, wherein        the volumetric model of scan-specific attenuation density        gradients within the lumen adjusts for reduced luminal density        across plaque lesions that are more functionally significant in        terms of risk value.    -   2. A ratio of plaque attenuation to fat attenuation, wherein        plaques with high radiodensities are considered to present a        lower risk, even within a subset of plaques considered        “calcified,” where there can be a gradation of densities (for        example, 130 to 4000 HU) and risk is considered to be reduced as        density increases.    -   3. A ratio of lumen attenuation/plaque attenuation/fat        attenuation.    -   4. A ratio of #1-3 as a function of 3D shape of atherosclerosis,        which can include a 3D texture analysis of the plaque.    -   5. The 3D volumetric shape and path of the lumen along with its        attenuation density from the beginning to the end of the lumen.    -   6. The totality of plaque and plaque types before and after any        given plaque to further inform its risk.    -   7. Determination of “higher plaque risks” by “subtracting”        calcified (high-density) plaques to obtain a better absolute        measure of high risk plaques (lower-density plaques). In other        words, this particular embodiment involves identifying calcified        plaque and excluding it from further analysis of plaque for the        purpose of identifying high risk plaques.

In some embodiments, the systems, devices, and methods described hereincan automatically and/or dynamically perform quantified analysis ofvarious parameters relating to plaque, cardiovascular arteries, and/orother structures. For example, rather than having a physician eyeball ormake a general assessment of the patient, a medical image can betransmitted to a backend main server in some embodiments that isconfigured to conduct such analyses, which advantageously can beperformed in a consistent, objective, and/or reproducible manner In someembodiments, the systems, methods, and devices described herein canprovide a quantified measurement of one or more features of a coronaryCT image using automated and/or dynamic processes. For example, in someembodiments, the main server system can be configured to identify one ormore vessels, plaque, and/or fat from a medical image. Based on theidentified features, in some embodiments, the system can be configuredto generate one or more quantified measurements from a raw medicalimage, such as for example density and/or radiodensity of one or moreregions of plaque, identification of stable plaque and/or unstableplaque, perivascular fat, pericoronary adipose tissue (PCAT), fatattenuation index (FAI), volumes thereof, surface areas thereof,geometric shapes, heterogeneity thereof, and/or the like. In someembodiments, the system can also generate one or more quantifiedmeasurements of vessels from the raw medical image, such as for examplediameter, volume, morphology, and/or the like.

Based on the identified features and/or quantified measurements, in someembodiments, the system can be configured to generate a risk assessmentand/or track the progression of a plaque-based disease or condition,such as for example atherosclerosis, stenosis, ischemia, myocardialinfarction, and/or major adverse cardiovascular event (MACE), using rawmedical images. As described further herein, in some embodiments thesystem can perform risk assessment and/or tracking the progression of aplaque-based disease based on other patients' information. For example,by comparing or evaluating features in a patient's medical images andpatient information (e.g., age, gender, BMI, medication, blood pressure,heart rate, height, weight, race, whether the patient is a smoker ornon-smoker, medical history, family history of disease, etc.) tofeatures in other patients' medical images and their associated patientinformation including their outcome after a period of time.

Further, in some embodiments, the system can be configured to generate avisualization of GUI of one or more identified features and/orquantified measurements, such as a quantized color mapping of differentfeatures. In some embodiments, the systems, devices, and methodsdescribed herein are configured to utilize medical image-basedprocessing to assess for a subject his or her risk of a cardiovascularevent, major adverse cardiovascular event (MACE), rapid plaqueprogression, and/or response to non-response to medication and/orlifestyle change and/or other treatment and/or invasive procedure. Inparticular, in some embodiments, the system can be configured toautomatically and/or dynamically assess such health risk of a subject byanalyzing only non-invasively obtained medical images. In someembodiments, one or more of the processes can be automated using anartificial intelligence (AI) and/or machine learning (ML) algorithm. Insome embodiments, one or more of the processes described herein can beperformed within minutes in a reproducible manner. This is starkcontrast to existing measures today which do not produce reproducibleprognosis or assessment, take extensive amounts of time, and/or requireinvasive procedures.

In some embodiments, image information comprising a plurality of imagesof a patient's coronary arteries and patient information/characteristicsmay be provided from one or more of the devices to the one or moreservers of the processing system via a network. The processing system isconfigured to generate coronary artery information using the pluralityof images of the patient's coronary arteries to generate two-dimensionaland/or three-dimensional data representations of the patient's coronaryarteries. Then, the processing system analyzes the data representationsto generate patient reports documenting a patient's health conditionsand risks related to coronary plaque. The patient reports may includeimages and graphical depictions of the patient's arteries in the typesof coronary plaque in or near the coronary arteries. Using machinelearning techniques or other artificial intelligent techniques, the datarepresentations of the patient's coronary arteries may be compared toother patients' data representations (e.g., that are stored in adatabase) to determine additional information about the patient'shealth. In some embodiments, the artificial intelligence can be trainedusing a dataset of other patients' data representations to identifycorrelations in data. For example, based on certain plaque conditions ofthe patient's coronary arteries, the likelihood of a patient having aheart attack or other adverse coronary effect can be determined. Also,for example, additional information about the patient's risk of CAD mayalso be determined.

In some embodiments, the coronary plaque information of a patient beingexamined may be compared to or analyzed in reference to a patient whohas one or more of the same or similar patient characteristics. Forexample, the patient being examined may be compared to a patient thathas the same or similar characteristics of sex, age, BMI, medication,blood pressure, heart rate, weight, height, race, body habitus, smoking,diabetes, hypertension, prior coronary artery disease, family history,and lab results. Such comparisons can be done through various means, forexample machine learning and/or artificial intelligence techniques. Insome examples, neural network is used to compare a patient's coronaryartery information to numerous (e.g., 10,000+) other patients' coronaryartery information. For such patients that have similar patientinformation and similar cardiac information, risk assessments of theplaque of the patient being examined may be determined.

In some embodiments, Deep Learning (DL) methods, machine learning (ML)methods, and artificial intelligence (AI) methods can be used to analyzeimage information. In an example, this analysis can comprise imagesegmentation, feature extraction, and classification. In someembodiments, ML methods can comprise image feature extraction andimage-based learning from raw data. In some embodiments, the ML methodcan receive an input of a large training set to learn to ignorevariations that could otherwise skew the results of the method. In someembodiments, DL can comprise a Neural Network (NN) with three or morelayers that can improve the accuracy of determinations. Advantageously,in some embodiments, DL can obviate the need for pre-processing dataand, instead, process raw data. For example, while a human may input ahierarchy of important features of coronary image information for a MLalgorithm to make determinations, DL algorithms can determine whichfeatures are important and use these features to make determinations.Advantageously, in some embodiments, a DL algorithm can adjust itselffor accuracy and precision. In some embodiments, ML and DL algorithmscan perform supervised learning, unsupervised learning, andreinforcement learning.

In some embodiments, NN approaches, including convolutional neuralnetworks (CNN) and recurrent convolutional neural networks (RCNN), amongothers, can be used to analyze information in a manner similar tohigh-level cognitive functions of a human mind. In some embodiments, aNN approach can comprise training an object recognition system numerousmedical images in order to teach it patterns in the images thatcorrelate with particular labels. In some embodiments, a CNN cancomprise a NN where the nodes of each layer are clustered, the clustersoverlap, and each cluster feeds data to multiple nodes of the nextlayer. In some embodiments, a RCNN can comprise a CNN where recurrentconnections are incorporated in each convolutional layer.Advantageously, in some embodiments, the recurrent connections can makeobject recognition a dynamic process despite the fact that the input isstatic.

In some embodiments, the vessel identification algorithm, coronaryartery identification algorithm, and/or plaque identification algorithmcan be trained on a plurality of medical images wherein one or morevessels, coronary arteries, and/or regions of plaque are pre-identified.Based on such training, for example by use of a CNN in some embodiments,the system can be configured to automatically and/or dynamicallyidentify from raw medical images the presence and/or parameters ofvessels, coronary arteries, and/or plaque. In some embodiments, thesystem can be configured to utilize one or more AI and/or ML algorithmsto identify and/or analyze vessels or plaque, derive one or morequantification metrics and/or classifications, and/or generate atreatment plan. In some embodiments, the system can be configured toutilize an AI and/or ML algorithm to identify areas in an artery thatexhibit plaque buildup within, along, inside and/or outside thearteries. In some embodiments, input to the AI and/or ML algorithms caninclude images of a patient and patient information (orcharacteristics), for example, one or more of age, gender, body massindex (BMI), medication, blood pressure, heart rate, height, weight,race, whether the patient is a smoker or non-smoker, body habitus (forexample, the “physique” or “body type” which may be based on a widerange of factors), medical history, diabetes, hypertension, priorcoronary artery disease (CAD), dietary habits, drug history, familyhistory of disease, information relating to other previously collectedimage information, exercise habits, drinking habits, lifestyleinformation, or lab results, and the like. In an example where a NN isused, the NN can be trained using information from a plurality ofpatients, where the information for each patient can include medicalimages and one or more patient characteristics.

In some embodiments, the system can be configured to utilize one or moreAI and/or ML algorithms to automatically and/or dynamically identify oneor more regions of plaque using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a CNN on a set of medical images on which regions of plaque havebeen identified, thereby allowing the AI and/or ML algorithmautomatically identify regions of plaque directly from a medical image.In some embodiments, the system can be configured to identify a vesselwall and a lumen wall for each of the identified coronary arteries inthe medical image. In some embodiments, the system is then configured todetermine the volume in between the vessel wall and the lumen wall asplaque. In some embodiments, the system can be configured to identifyregions of plaque based on the radiodensity values typically associatedwith plaque, for example by setting a predetermined threshold or rangeof radiodensity values that are typically associated with plaque with orwithout normalizing using a normalization device.

In some embodiments, the one or more vascular morphology parametersand/or plaque parameters can comprise quantified parameters derived fromthe medical image. For example, in some embodiments, the system can beconfigured to utilize an AI and/or ML algorithm or other algorithm todetermine one or more vascular morphology parameters and/or plaqueparameters. As another example, in some embodiments, the system can beconfigured to determine one or more vascular morphology parameters, suchas classification of arterial remodeling due to plaque, which canfurther include positive arterial remodeling, negative arterialremodeling, and/or intermediate arterial remodeling. In someembodiments, the classification of arterial remodeling is determinedbased on a ratio of the largest vessel diameter at a region of plaque toa normal reference vessel diameter of the same region which can beretrieved from a normal database. In some embodiments, the system can beconfigured to classify arterial remodeling as positive when the ratio ofthe largest vessel diameter at a region of plaque to a normal referencevessel diameter of the same region is more than 1.1. In someembodiments, the system can be configured to classify arterialremodeling as negative when the ratio of the largest vessel diameter ata region of plaque to a normal reference vessel diameter is less than0.95. In some embodiments, the system can be configured to classifyarterial remodeling as intermediate when the ratio of the largest vesseldiameter at a region of plaque to a normal reference vessel diameter isbetween 0.95 and 1.1.

In some embodiments, the system is configured to classifyatherosclerosis of a subject based on the quantified atherosclerosis asone or more of high risk, medium risk, or low risk. In some embodiments,the system is configured to classify atherosclerosis of a subject basedon the quantified atherosclerosis using an AI, ML, and/or otheralgorithm. In some embodiments, the system is configured to classifyatherosclerosis of a subject by combining and/or weighting one or moreof a ratio of volume of surface area, volume, heterogeneity index, andradiodensity of the one or more regions of plaque.

In some embodiments, the system can be configured to identify one ormore regions of fat, such as epicardial fat, in the medical image, forexample using one or more AI and/or ML algorithms to automaticallyand/or dynamically identify one or more regions of fat. In someembodiments, the one or more AI and/or ML algorithms can be trainedusing a CNN on a set of medical images on which regions of fat have beenidentified, thereby allowing the AI and/or ML algorithm automaticallyidentify regions of fat directly from a medical image. In someembodiments, the system can be configured to identify regions of fatbased on the radiodensity values typically associated with fat, forexample by setting a predetermined threshold or range of radiodensityvalues that are typically associated with fat with or withoutnormalizing using a normalization device.

In some embodiments, the system is configured to utilize an AI, ML,and/or other algorithm to characterize the change in calcium score basedon one or more plaque parameters derived from a medical image. Forexample, in some embodiments, the system can be configured to utilize anAI and/or ML algorithm that is trained using a CNN and/or using adataset of known medical images with identified plaque parameterscombined with calcium scores. In some embodiments, the system can beconfigured to characterize a change in calcium score by accessing knowndatasets of the same stored in a database. For example, the knowndataset may include datasets of changes in calcium scores and/or medicalimages and/or plaque parameters derived therefrom of other subjects inthe past. In some embodiments, the system can be configured tocharacterize a change in calcium score and/or determine a cause thereofon a vessel-by-vessel basis, segment-by-segment basis, plaque-by-plaquebasis, and/or a subject basis.

In some embodiments, the systems disclosed herein can be used todynamically and automatically determine a necessary stent type, length,diameter, gauge, strength, and/or any other stent parameter for aparticular patient based on processing of the medical image data, forexample using AI, ML, and/or other algorithms.

In some embodiments, the system can be configured to utilize an AIand/or ML algorithm to generate the patient-specific report. In someembodiments, the patient-specific report can include a document, ARexperience, VR experience, video, and/or audio component.

FIG. 8A is a block diagram that illustrates an example of a systemand/or process 800 (both referred to here as a “system” for ease ofreference) for identifying features and/or risk information of a patientusing AI/ML based on non-invasively obtained medical images of thepatient and/or patient information. A current patient's medical dataincluding images and/or patient information is first obtained andelectronically stored on medical data storage 816 (e.g., cloud storage,hard disk, etc.). The system 800 obtains medical images and/or patientinformation 818 from the medical data storage 816 and preprocess it, ifnecessary, for example to re-format it as necessary for furtherprocessing. The system 800 can also obtain a training set of medicalimages and/or patient information 822 from a stored dataset 820 ofmedical images and/or information of other patients (e.g., hundreds,thousands, tens of thousands, or hundreds of thousands or more of otherpatients). The medical images and information of other patients can beused to train the AI/ML algorithm 824 prior to processing the medicalimages and/or patient information 818 of the current patient, asdescribed in further detail in reference to FIGS. 8C and 8D. In someembodiments, the AI/ML algorithm 824 can include one or more NN's, forexample, as described in reference to the example NN illustrated in FIG.8B. The ML/AI 824 processes the medical images and/or patientinformation 818 of the current patient and generates outputs ofidentified features and/or risk information 826 of the current patient.

FIG. 8B is a schematic illustrating an example of a NN 812 that makesdeterminations 814 about characteristics of a (current) patient based oninputs that include medical images 802. In some embodiments, the NN 812can be configured to receive other inputs 804. In some embodiments, theother inputs 804 can be medical images of other patients. In someembodiments, the other inputs 804 can be medical history of otherpatients. In some embodiments, the other inputs 804 can be medicalhistory of the (current) patient. The NN 812 can include an input layer806. In some embodiments, the NN 812 can be configured to present thetraining pattern to the input layer 806. In some embodiments, the NN 812can include one or more hidden layers 808. In some embodiments, theinput layer 806 can provide signals to the hidden layers 808, and thehidden layers 808 can receive signals from the input layer 806. In someembodiments, the hidden layers 808 can pass signals to the output layer810. In some embodiments, one or more hidden layers 808 may beconfigured as convolutional layers (comprising neurons/nodes connectedby weights, the weights corresponding to the strength of the connectionbetween neurons), pooling layers, fully connected layers and/ornormalization layers. In some embodiments, the NN 812 may be configuredwith pooling layers that combine outputs of neuron clusters at one layerinto a single neuron in the next layer. In some embodiments, max poolingand/or average pooling may be utilized. In some embodiments, max poolingmay utilize the maximum value from each of a cluster of neurons at theprior layer. In some embodiments, back propagation may be utilized, andthe corresponding neural network weights may be adjusted to minimize orreduce the error. In some embodiments, the loss function may comprisethe Binary Cross Entropy loss function.

In some embodiments, the NN 812 can include an output layer 810. In someembodiments, the output layer 810 can receive signals from the hiddenlayers 808. In some embodiments, the output layer can generatedeterminations 814. In some embodiments, the NN 812 can makedeterminations 814 about characteristics of the patient. In someembodiments, the determinations 814 can include a characterized set ofplaque. In some embodiments, the determinations 814 can include apatient's risk of CAD.

FIG. 8C depicts an example of a process in a flow diagram for trainingan artificial intelligence or machine learning model. The process 828can be performed on a computing system. Various embodiments of such aprocess for training an AI or ML model may include additional features,and/or may exclude certain illustrated features (for example, when atransformed dataset is accessed such that “apply transformations” inblock 832 does not need to be performed.)

As illustrated in the example of FIG. 8C, at block 830 the systemreceives a dataset that includes patient health information which caninclude medical images, user surveys, historical test results, geneticinformation, and/or other patient information (e.g., height, weight,age, etc.). The dataset can also include non-health information, forexample, employment information, income information, transportationinformation, housing information, distances to pharmacies, and/ordistances to healthcare providers.

At block 832, one or more transformations may be performed on the data.In an example, data may require transformations to conform to expectedinput formats to conform with expected formatting, e.g., dateformatting, units (e.g., pounds vs kilograms, Celsius vs Fahrenheit,inches vs centimeters, etc.), address conventions, be of a consistentformat, and the like. In some embodiments, addresses can be converted,or altered, to be of a consistent format and/or to conform to standardspublished by the United States Postal Service or a similar postalauthority. In some embodiments, the data may undergo conversions toprepare it for use in training an AI or ML algorithm, for example,categorical data may be encoded in a particular manner In someembodiments, nominal data may be encoded using one-hot encoding, binaryencoding, feature hashing, or other suitable encoding methods. In someembodiments, ordinal data may be encoded using ordinal encoding,polynomial encoding, Helmert encoding, and so forth. In someembodiments, numerical data may be normalized, for example by scalingdata to a maximum of 1 and a minimum of 0 or −1. These are merelyexamples, and the skilled artisan will readily appreciate that othertransformations are possible.

At block 834, the system may create, from the received dataset,training, tuning, and testing/validation datasets. In some embodiments,the training dataset 836 may be used during training to determinefeatures for forming a predictive model. In some embodiments, the tuningdataset 838 may be used to select final models and to prevent or correctoverfitting that may occur during training with the training dataset836, as the trained model should be generally applicable to a broadspectrum of patients. In some embodiments, the testing dataset 840 maybe used after training and tuning to evaluate the model. For example, insome embodiments, the testing dataset 840 may be used to check if themodel is overfitted to the training dataset. In some embodiments, thesystem, in training loop 856, may train the model at block 842 using thetraining dataset 836. In some embodiments, training may be conducted ina supervised, unsupervised, or partially supervised manner. At 844, insome embodiments, the system may evaluate the model according to one ormore evaluation criteria. For example, in some embodiments, theevaluation may include determining how often the model determinesreasonable scores for a patient's risk of CAD. At 846, in someembodiments, the system may determine if the model meets the one or moreevaluation criteria. In some embodiments, if the model fails evaluation,the system may, at 848, tune the model using the tuning dataset 838,repeating the training 842 and evaluation 844 until the model passes theevaluation at 846. In some embodiments, once the model passes theevaluation at 846, the system may exit the model training loop 856. Insome embodiments, the testing dataset 836 may be run through the trainedmodel 842 and, at block 844, the system may evaluate the results. Insome embodiments, if the evaluation fails, at block 846, the system mayreenter training loop 856 for additional training and tuning. If themodel passes, the system may stop the training process, resulting in atrained model 850. In some embodiments, the training process may bemodified. For example, in some embodiments, the system may not use atuning dataset 838. In some embodiments, the model may not use a testingdataset 840.

While described above with respect to determining risk scores for CAD, amodel can be trained for use in a wide variety of problems.

FIG. 8D illustrates an example of a process for training and using anAI/ML model. In some embodiments, the process of FIG. 8D can be used forvarious purposes, e.g., to determine risk scores of CAD for a patient orto characterize plaque. In some embodiments, training data store 858 canstore data for training a model. For example, in some embodiments,training data store 858 can store a patient's medical images, as well asinformation about patient's health, age, socioeconomic status,employment status, housing arrangements, transportation, and so forth.In some embodiments, the training data can be annotated to includeinformation about user outcomes. For example, in some embodiments, theuser outcomes can indicate whether a user had to miss work due toillness, was hospitalized, visited an emergency room, visited an urgentcare facility, and so forth. In some embodiments, the training data canindicate whether a user received medication to treat an illness at home,treatments delivered at a hospital or other healthcare facility, did notreceive any treatment, and so forth. At block 860, in some embodiments,a system can be configured to prepare the training data if it was notpreviously prepared for use in training a model. In some embodiments, asdescribed briefly above, preparing the training data can includeperforming one or more normalization procedures, standardizationprocedures, and so forth, such as converting units (e.g., betweenFahrenheit and Celsius, between inches and centimeters, between poundsand kilograms), converting dates to a standard format, converting timesto a standard format, and so forth. In some embodiments, similartreatments or symptoms may be described or coded differently bydifferent healthcare providers. In some embodiments, different providersmay use different coding schemes. In some embodiments, even within aparticular coding scheme, providers may select different codes toindicate similar information. In some embodiments, a large number ofsimilar codes can lead to variances in coding. Thus, in someembodiments, a code can be changed to another related code. In someembodiments, certain codes can be excluded if they are not relevant tothe issue that the model is intended to address. In some embodiments, itcan be desirable to exclude certain data as additional data can consumeadditional computing resources and it can take longer to train a model.However, in some embodiments, exclusions may not be desirable as therecan be a risk of excluding a factor that actually is relevant to thepatient's risk. In some embodiments, data preparation at block 860 caninclude modifying or removing coding data, treatment data, and so forth.At block 862, the system can extract features from the training dataand, at block 864, can train the model using the training data toproduce model 866. At block 868, in some embodiments, the system canevaluate the model to determine if it passes one or more criteria. Insome embodiments, at decision point 870, if the model fails, the systemcan perform additional training. In some embodiments, if, at decisionpoint 870, the model passes, the system can make available trained model872, which can be the model 872 after training is complete.

In some embodiments, the trained model 872 can be used to evaluate aparticular user. The user data 874 can relate to a specific user forwhom the outputs of the trained model 872 are desired. At block 876, thesystem can prepare the data, for example as described above in relationsto the stored training data. In some embodiments, at block 878, thesystem can extract features from the prepared user data. In someembodiments, the system can be configured to feed the extracted featuresto the trained model 872 to produce results 880. The results 880 can beused to, for example, to determine a risk level associated with the userand/or to determine one or more risk sub-scores for the user.

In some embodiments, the user data 874, the results 880, and otherinformation about the user (e.g., information about the user's outcomesafter either receiving or not receiving treatment for plaque-baseddisease) can be used to train the model. At block 882, in someembodiments, the system can user prepare the user data 874 and theresults 880 for use in training. In some embodiments, preparing the datacan include, for example, anonymizing the data. For example, in someembodiments, any information about the patient's name, social securitynumber, or other information that could personally identify the patientcan be removed. In some embodiments, the system can anonymize the userdata 874 in part by altering the user's birthday, for example retainingonly the year the user was born (as age is often an important factor inevaluating ask) or the year and month the user was born. In someembodiments, the system can store the prepared data in training datastore 858. In some embodiments, the prepared data can be stored,additionally or alternatively, in another database or data store. Insome embodiments, the system can retrain the model on periodically,continuously, or whenever an operator indicates to the system that themodel should be retrained. Thus, in some embodiments, the trained model872 can evolve over time, which can result in, for example, improvedrisk evaluation over time as the model is trained on additional data.

Plaque Morphology/Features Analysis

As discussed herein, disclosed herein are systems, methods, and devicesfor non-invasive image-based plaque analysis and risk determination. Inparticular, in some embodiments, the systems, devices, and methodsdescribed herein are related to analysis of one or more regions ofplaque, such as for example coronary plaque, based on one or moredistances, volumes, shapes, morphologies, embeddedness, and/or axesmeasurements. For example, in some embodiments, the systems, devices,and methods described herein are related to plaque analysis based on oneor more of distance between plaque and vessel wall, distance betweenplaque and lumen wall, length along longitudinal axis, length alonglatitudinal axis, volume of low density non-calcified plaque, volume oftotal plaque, a ratio(s) between volume of low density non-calcifiedplaque and volume of total plaque, embeddedness of low densitynon-calcified plaque, and/or the like. In some embodiments, the systems,devices, and methods described herein are configured to determine a riskof coronary artery disease (CAD), such as for example myocardialinfarction (MI), based on one or more plaque analyses described herein.In some embodiments, the systems, devices, and methods described hereinare configured to generate a proposed treatment and/or graphicalrepresentation based on the determined risk of CAD and/or one or moreplaque analyses described herein.

More specifically, in some embodiments, the systems, methods, anddevices can be configured to analyze a medical image to perform one ormore analyses of plaque and/or types of plaque, such as for example lowdensity non-calcified plaque, calcified plaque, non-calcified plaque,and/or the like. In particular, in some embodiments, low densitynon-calcified plaque can be a focus due to the high-risk generallyassociated with low density non-calcified plaque. For example, lowdensity non-calcified plaque can have a higher risk of potential rupturecompared to other types of plaque, such as regular non-calcified plaqueor calcified plaque. A plaque rupture can, in some instances, clog orblock a vessel, thereby causing a heart attack or MI. As such, it can beadvantageous to analyze one or more features of low densitynon-calcified plaque, and/or non-calcified plaque and/or calcifiedplaque, which may correspond to high or low risk of CAD and/or stabilityor instability of plaque. In some embodiments, the systems, devices, andmethods are configured to analyze a medical image, such as a CT or CCTAimage, to derive one or more features, measures, and/orcharacterizations of plaque, such as low density non-calcified plaque,non-calcified plaque, and/or calcified plaque, and use the same tofacilitate an assessment or and/or generate an assessment of risk of CADand/or stability or instability of plaque. Thus, in some embodiments,the systems, devices, and methods can provide an efficient and/ornon-invasive method of assessing risk of CAD and/or plaque.

FIG. 9A illustrates examples of sample measurements, dimensions, and/oranalyses related to plaque analysis and risk determination. For example,FIG. 9A(a) illustrates an example of low-density non-calcified (LDNC)aggregates associated with a single coronary lesion. FIG. 9A(b)illustrates an example of distances of plaque to lumen and vessel walls.FIG. 9A(c) illustrates an example of dimension axis measurements ofplaque along a major axis and along a minor axis. FIG. 9A(d) illustratesexamples of degrees of embeddedness of LDNC plaque (DELP). FIG. 9A(e)illustrates examples of four different shapes of plaques or regions ofplaque, including plaque that is crescent-shaped, generally round,lobular in shape, and bean-shaped.

As illustrated in FIG. 9A, in some embodiments, the system can beconfigured to analyze a medical image of a vessel, artery, and/or aportion thereof, such as a coronary lesion. In some embodiments, aparticular vessel, artery, and/or lesion can comprise one or moreregions of plaque. “Density” and “radiodensity” generally relate to theopacity of a particular material (i.e., the relative inability ofradiation to pass through the particular material), that is depicted ina medical image, where the higher the density/radiodensity the higherthe opacity of the material. In some embodiments, based on the absoluteor material density and/or relative density and/or radiodensity offeatures in one or more medical images, the system can be configured tocharacterize a region of plaque into one or more sub-types of plaque.For example, in some embodiments, the system can be configured tocharacterize a region of plaque as either low density non-calcifiedplaque, non-calcified plaque, or calcified plaque. In some embodimentsthe system can be configured to characterize a region of plaque to beone or more of low density non-calcified plaque, non-calcified plaque,or calcified plaque. In some embodiments, calcified plaque cancorrespond to plaque having a highest density range, low densitynon-calcified plaque can correspond to plaque having a lowest densityrange, and non-calcified plaque can correspond to plaque having adensity range between calcified plaque and low density non-calcifiedplaque. For example, in some embodiments, the system can be configuredto characterize a particular region of plaque as low densitynon-calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about −189 and about30 Hounsfield units (HU). In some embodiments, the system can beconfigured to characterize a particular region of plaque asnon-calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about 31 and about 350HU. In some embodiments, the system can be configured to characterize aparticular region of plaque as calcified plaque when the radiodensity ofan image pixel or voxel corresponding to that region of plaque isbetween about 351 and about 2500 HU. In some embodiments, the lowerand/or upper Hounsfield unit boundary threshold for determining whethera plaque corresponds to one or more of low density non-calcified plaque,non-calcified plaque, and/or calcified plaque can be about −1000 HU,about −900 HU, about −800 HU, about −700 HU, about −600 HU, about −500HU, about −400 HU, about −300 HU, about −200 HU, about −190 HU, about−180 HU, about −170 HU, about −160 HU, about −150 HU, about −140 HU,about −130 HU, about −120 HU, about −110 HU, about −100 HU, about −90HU, about −80 HU, about −70 HU, about −60 HU, about −50 HU, about −40HU, about −30 HU, about −20 HU, about −10 HU, about 0 HU, about 10 HU,about 20 HU, about 30 HU, about 40 HU, about 50 HU, about 60 HU, about70 HU, about 80 HU, about 90 HU, about 100 HU, about 110 HU, about 120HU, about 130 HU, about 140 HU, about 150 HU, about 160 HU, about 170HU, about 180 HU, about 190 HU, about 200 HU, about 210 HU, about 220HU, about 230 HU, about 240 HU, about 250 HU, about 260 HU, about 270HU, about 280 HU, about 290 HU, about 300 HU, about 310 HU, about 320HU, about 330 HU, about 340 HU, about 350 HU, about 360 HU, about 370HU, about 380 HU, about 390 HU, about 400 HU, about 410 HU, about 420HU, about 430 HU, about 440 HU, about 450 HU, about 460 HU, about 470HU, about 480 HU, about 490 HU, about 500 HU, about 510 HU, about 520HU, about 530 HU, about 540 HU, about 550 HU, about 560 HU, about 570HU, about 580 HU, about 590 HU, about 600 HU, about 700 HU, about 800HU, about 900 HU, about 1000 HU, about 1100 HU, about 1200 HU, about1300 HU, about 1400 HU, about 1500 HU, about 1600 HU, about 1700 HU,about 1800 HU, about 1900 HU, about 2000 HU, about 2100 HU, about 2200HU, about 2300 HU, about 2400 HU, about 2500 HU, about 2600 HU, about2700 HU, about 2800 HU, about 2900 HU, about 3000 HU, about 3100 HU,about 3200 HU, about 3300 HU, about 3400 HU, about 3500 HU, and/or about4000 HU.

In some embodiments, the system can be configured to characterize one ormore regions of plaque and generate a display of the same on astraightened multiplanar view or a multiplanar view of a vessel, forexample as illustrated in FIG. 9A(a). In the example illustrated in FIG.9A(a), this particular coronary lesion comprises regions ofnon-calcified plaque 901 with one or more regions of calcified plaque903 and two regions of low density non-calcified plaque 905. In someembodiments, the systems, methods, and devices can be configured toperform one or more analyses on one or more regions of plaque, which canbe a factor and/or indicator of the stability of plaque and/or risk ofCAD or MI. For example, in some embodiments, the systems, methods, anddevices can be configured to determine a distance from a region of lowdensity non-calcified plaque to the vessel wall and/or lumen wall. In anexample, the distance can be the shortest distance from low densitynon-calcified plaque to the vessel wall and/or lumen wall, or in anotherexample, the distance can be the average distance from multiple pointsof low density non-calcified plaque to the vessel wall and/or lumenwall. This is further described in reference to FIG. 9A(b) below. Insome embodiments, and as described further in reference to FIG. 9A(c),the systems, methods, and devices can be configured to determine one ormore dimensions, such as for example major and/or minor axes, of aregion of low density non-calcified plaque. In some embodiments, and asdescribed further in reference to FIG. 9A(d), the systems, methods, anddevices can be configured to determine the degree of embeddedness of alow density non-calcified plaque. In some embodiments, and as describedfurther in reference to FIG. 9A(e), the systems, methods, and devicescan be configured to determine a shape or morphology of low densitynon-calcified plaque. In some embodiments, the systems, methods, anddevices can be configured to determine a volume or size of low densitynon-calcified plaque, total plaque, and/or a ratio thereof. In someembodiments, the systems, methods, and devices can be configured to takeinto account one or some or all of the aforementioned analyses inassessing the stability or instability of plaque and/or risk of CAD orMI on a patient or subject basis, vessel basis, lesion basis, and/or thelike. In some embodiments, the systems, methods, and devices can beconfigured to generate a weighted measure of one or some or all of theaforementioned analyses in assessing the stability or instability ofplaque and/or risk of CAD or MI on a patient or subject basis, vesselbasis, lesion basis, and/or the like. In some embodiments, the systems,methods, and devices can be configured to utilize one or more artificialintelligence (AI) and/or machine learning (ML) algorithms in performingany one or more of the analyses described herein.

In particular, in some embodiments, the systems, methods, and devicescan be configured to measure and/or determine a distance from a regionof low density non-calcified plaque to the lumen and/or vessel walls. Insome embodiments, the distance from a region of low densitynon-calcified plaque to the lumen and/or vessel walls can be indicativeof the stability of the low density non-calcified plaque and hence beconsidered a factor in determining the risk of CAD arising from that lowdensity non-calcified plaque. For example, in some embodiments, a lowdensity non-calcified plaque that is closer to the lumen wall can beconsidered more susceptible to rupture and cause an MI.

In particular, in some embodiments, the system can be configured todetermine the shortest distance between a point in the boundary of theregion of low density non-calcified plaque and the vessel wall and/orlumen wall. In some embodiments, the system can be configured todetermine the distance between a low density non-calcified plaque andthe vessel wall and/or lumen wall based on a two-dimensional slice imageand/or a three-dimensional rendering.

For example, as illustrated in the example of FIG. 9A(b), in someembodiments, the system can be configured to analyze a two-dimensionalslice that renders the largest cross-sectional area of low densitynon-calcified plaque. In some embodiments, the two-dimensional slice canbe perpendicular to a longitudinal axis of the straightened multiplanarview of a vessel. In some embodiments, the system can be configured toautomatically and/or semi-automatically determine a distance from a lowdensity non-calcified plaque to the vessel and/or lumen wall. In someembodiments, the system can be configured to assist a user indetermining a distance from a low density non-calcified plaque to thevessel and/or lumen wall. For example, in some embodiments, the systemcan be configured to generate a graphical user interface that allows auser to click on two points on the image, after which the system canautomatically determine the distance between the two points. In someembodiments, the system can be configured to utilize a digital caliperto determine a distance between low density non-calcified plaque andvessel or lumen wall.

FIG. 9A(b) illustrates the distance from low density non-calcifiedplaque to the lumen wall 907, and the distance from low densitynon-calcified plaque to the vessel wall 909. In some embodiments, thesystem can be configured to average multiple distances from one or morelumen and/or vessel walls to determine the distance from the region ofplaque to the lumen and/or vessel wall. In some embodiments, theboundary of the region of low density non-calcified plaque can bedetermined at an end of the gradient of plaque densities partiallycomprising low density non-calcified plaque furthest from the center ofthe region of low density non-calcified plaque. In some embodiments, theboundary of the region of low density non-calcified plaque can bedetermined at an end of the gradient of plaque densities partiallycomprising low density non-calcified plaque closest to the center of theregion of low density non-calcified plaque. In some embodiments, theboundary of the region of low density non-calcified plaque can bedetermined within the gradient of plaque densities partially comprisinglow density non-calcified plaque between the furthest point from andclosest point to the center of the region of low density non-calcifiedplaque. In some embodiments, a shorter distance between the boundary ofthe region of low density non-calcified plaque and the lumen and/orvessel wall is indicative of a higher risk of CAD or MI. In someembodiments, a higher distance between the boundary of the region of lowdensity non-calcified plaque and the lumen and/or vessel wall isindicative of a lower risk of CAD or MI. For example, in someembodiments, the system can be configured to characterize risk of CAD orMI as high or low and/or risk of plaque as high or low when a distance,such as the shortest distance, between the boundary of the region oflow-density non-calcified plaque and the lumen and/or vessel wall isabove or below about 0.0 mm, about 0.1 mm, about 0.2 mm, about 0.3 mm,about 0.4 mm, about 0.5 mm, about mm, about 0.7 mm, about 0.8 mm, about0.9 mm, about 1.0 mm, about 1.1 mm, about 1.2 mm, about 1.3 mm, about1.4 mm, about 1.5 mm, about 1.6 mm, about 1.7 mm, about 1.8 mm, about1.9 mm, about 2.0 mm, and/or any number between, above, or below any ofthe foregoing.

In some embodiments, the systems, methods, and devices are configured todetermine one or more dimensions of a low density non-calcified plaque,as illustrated in FIG. 9A(c). For example, in some embodiments, thesystem can be configured to measure and/or determine the length of amajor and/or minor axis of a region of low density non-calcified plaque.In some embodiments, the system can be configured to measure and/ordetermine the length of a major and/or minor axis of a region of lowdensity non-calcified plaque automatically or semi-automatically, forexample using an image processing algorithm. In some embodiments, thesystem can be configured to measure and/or determine the length of amajor and/or minor axis of a region of low density non-calcified plaquebased at least in part on user input, for example using a digitalcaliper. In some embodiments, the system can be configured to measureand/or determine the length of a major and/or minor axis of a region oflow density non-calcified plaque based on a three-dimensional analysisand/or based on one or more two-dimensional images.

For example, as illustrated in the example of FIG. 9A(c), in someembodiments, the system is configured to take a longitudinal slice imageof a region of plaque and measure the major and/or minor axis of lowdensity non-calcified plaque. In some embodiments, the longitudinalslice can be taken such that it is parallel to a longitudinal axis of amultiplanar or straightened multiplanar view of a vessel. In someembodiments, the longitudinal slice can be taken such that it shows thelongest possible longitudinal axis of the low density non-calcifiedplaque. In some embodiments, the system can be configured to take alatitudinal slice image of a region of plaque and measure the majorand/or minor axis of low density non-calcified plaque. In someembodiments, the latitudinal slice can be taken such that isperpendicular or orthogonal to the longitudinal axis of a multiplanar orstraightened multiplanar view of a vessel and/or longitudinal slice. Insome embodiments, the longitudinal slice can be taken such that it showsthe largest possible cross-sectional area of low density non-calcifiedplaque. In some embodiments, the system can be configured to determinemeasurements of one or more orthogonal axes of a low densitynon-calcified plaque.

In some embodiments, such measurements of one or more axes of a regionof low density non-calcified plaque can be utilized by the system forfurther characterization and/or analysis. For example, in someembodiments, measurement(s) of one or more axes of a region of lowdensity non-calcified plaque can be used as a factor in assessing thestability of plaque and/or risk of CAD or MI of the subject, eitherdirectly or indirectly. In some embodiments, the system can beconfigured to utilize one or more measurements of one or more axes of aregion of low density non-calcified plaque to determine and/or estimatethe size, volume, and/or shape of low density non-calcified plaque,which in turn can be correlated to an assessment of the stability ofplaque and/or risk of CAD or MI. In some embodiments, the system can beconfigured to directly correlate one or more measurements of one or moreaxes of a region of low density non-calcified plaque to an assessment ofthe stability of plaque and/or risk of CAD or MI. In some embodiments, aregion of low density non-calcified plaque with a greater area, length,diameter, or volume can correspond to a higher risk of CAD or MI. Insome embodiments, a region of low density non-calcified plaque with asmaller area, length, diameter, or volume can correspond to a lower riskof CAD or MI. For example, in some embodiments, the system can beconfigured to characterize risk of CAD or MI as high or low and/or riskof plaque as high or low when the length or diameter of a region oflow-density non-calcified plaque is above or below about 0.0 mm, about0.1 mm, about 0.2 mm, about 0.3 mm, about 0.4 mm, about 0.5 mm, about0.6 mm, about 0.7 mm, about 0.8 mm, about 0.9 mm, about 1.0 mm, about1.1 mm, about 1.2 mm, about 1.3 mm, about 1.4 mm, about 1.5 mm, about1.6 mm, about 1.7 mm, about 1.8 mm, about 1.9 mm, about 2.0 mm, and/orany number between, above, or below any of the foregoing, and/orcorresponding area and/or volume. For example, in some embodiments, thesystem can be configured to characterize risk of CAD or MI as high orlow and/or risk of plaque as high or low when the area of a region oflow-density non-calcified plaque is above or below about 0.0 mm², about0.01 mm², about 0.04 mm², about 0.09 mm², about 0.16 mm², about 0.25mm², about 0.36 mm², about 0.49 mm², about 0.64 mm², about 0.81 mm²,about 1.0 mm², about 1.21 mm², about 1.44 mm², about 1.69 mm², about1.96 mm², about 2.25 mm², about 2.56 mm², about 2.89 mm², about 3.24mm², about 3.61 mm², about 4.0 mm², and/or any number between, above, orbelow any of the foregoing, and/or corresponding length, diameter and/orvolume. For example, in some embodiments, the system can be configuredto characterize risk of CAD or MI as high or low and/or risk of plaqueas high or low when the volume of a region of low-density non-calcifiedplaque is above or below about 0.0 mm³, about 0.000001 mm³, about0.000008 mm³, about 0.000027 mm³, about 0.0.000064 mm³, about 0.000125mm³, about 0.0.000216 mm³, about 0.0.000343 mm³, about 0.000343 mm³,about 0.0.000729 mm³, about 1.0 mm³, about 1.331 mm³, about 1.728 mm³,about 2.197 mm³, about 2.744 mm³, about 3.375 mm³, about 4.096 mm³,about 4.913 mm³, about 4.913 mm³, about 6.859 mm³, about 8.0 mm³, and/orany number between, above, or below any of the foregoing, and/orcorresponding length, diameter and/or area.

In some embodiments, the systems, methods, and devices can be configuredto determine an embeddedness of low density non-calcified plaque, forexample by regular non-calcified plaque and/or calcified plaque. In someembodiments, the embeddedness of low density non-calcified plaque can bea factor and/or indicator of the stability of plaque and/or risk of CADor MI, as a more embedded a low-density non-calcified plaque can be morelikely to rupture and/or have a thin fibrous cap or more likely todevelop a thin fibrous cap. In some embodiments, the system can beconfigured to determine the embeddedness of low density non-calcifiedplaque based on the degree of encapsulation of the low densitynon-calcified plaque by non-calcified plaque or calcified plaque. Insome embodiments, the embeddedness of the low density non-calcifiedplaque is determined by measuring how much of the boundary of the regionof the low density non-calcified plaque contacts other types of plaquein relation to how much of the boundary of the region of the low densitynon-calcified plaque contacts the vessel wall or lumen. In someembodiments, the system can be configured to measure and/or determinethe embeddedness of a region of low density non-calcified plaque basedon a three-dimensional analysis and/or based on one or moretwo-dimensional images.

For example, as illustrated in the example of FIG. 9A(d), in someembodiments, the system is configured to take a longitudinal slice imageof a region of plaque and measure the embeddedness of low densitynon-calcified plaque. In some embodiments, the longitudinal slice can betaken such that it is parallel to a longitudinal axis of a multiplanaror straightened multiplanar view of a vessel. In some embodiments, thesystem can be configured to take a latitudinal slice image of a regionof plaque and measure the embeddedness of low density non-calcifiedplaque. In some embodiments, the latitudinal slice can be taken suchthat it is perpendicular or orthogonal to the longitudinal axis of amultiplanar or straightened multiplanar view of a vessel and/orlongitudinal slice. In an example, when one half of the boundary of theregion of the low density non-calcified plaque is in contact with thevessel wall and the other half of the boundary of the region of the lowdensity non-calcified plaque is in contact with calcified plaque, theembeddedness is 180°. In another example, when three-quarters of theboundary of the region of the low density non-calcified plaque is incontact with the vessel wall and the other quarter of the boundary ofthe region of the low density non-calcified plaque is in contact withcalcified plaque, the embeddedness is 90°. In another example, when onequarter of the boundary of the region of the low density non-calcifiedplaque is in contact with the vessel wall and the other three quartersof the boundary of the region of the low density non-calcified plaque isin contact with calcified plaque, the embeddedness is 270°. In anotherexample, when all of the boundary of the region of the low densitynon-calcified plaque is in contact with calcified plaque, theembeddedness is 360°. In some embodiments, the system can be configuredto determine the embeddedness of low density non-calcified plaque as oneor more of about 0°, about 30°, about 60°, about 90°, about 120°, about150°, about 180°, about 210°, about 240°, about 270°, about 300°, about330°, about 360°, and/or between a range defined by two of theaforementioned values. In the example illustrated in FIG. 9A(d), in someembodiments, the embeddedness of low density non-calcified plaque isdetermined to be about 90°, about 180°, about 270°, about 360° in eachexample. In some embodiments, an embeddedness of low densitynon-calcified plaque at or above about 270° can be considered ahigh-risk plaque. In some embodiments, the system can be configured todetermine the embeddedness of a region of low density non-calcifiedplaque automatically or semi-automatically, for example using an imageprocessing algorithm. In some embodiments, the system can be configuredto determine the embeddedness of a region of low density non-calcifiedplaque based at least in part on user input. In some embodiments, thesystem can be configured to graphically overlay a protractor on amedical image to assist with and/or to display the embeddedness of aregion of low density non-calcified plaque.

In some embodiments, the systems, methods, and devices can be configuredto determine shape or morphology of a region of low densitynon-calcified plaque. In some embodiments, the system can be configuredto determine and/or characterize the shape or morphology of a lowdensity non-calcified plaque as one or more of a crescent, round,lobular, or bean shape. In some embodiments, the shape or morphology ofa low density non-calcified plaque can be a factor and/or indicator ofplaque stability and/or risk of CAD. In some embodiments, a crescentshape can have a greater length than width. In some embodiments, acrescent shape can be a thin, curved shape. In some embodiments, acrescent shape can be a narrow shape. In some embodiments, a crescentshape can be a shape resembling a curved or uncurved line. For example,in some embodiments, a crescent-shaped low density non-calcified plaquecan be associated with lower risk of CAD or MI. In some instances, thiscan be because what appears to be a crescent-shaped plaque due to imageanalysis can potentially correspond to perivascular fat rather than atrue low density non-calcified plaque, especially for example if foundaround or near or adjacent to the vessel wall. In some embodiments, around shape can have a similar or approximately equal length and width.In some embodiments, a round shape can have a similar or approximatelyconsistent diameter. In some embodiments, a round shape can be a shapesimilar to a circle or oval. In some embodiments, a bean shape can be ashape comprised of two or three interconnected round and/or crescentshapes. In some embodiments, a bean shape can be a wide shape comprisedof interconnected round and/or crescent shapes. In some embodiments, abean shape can be a distorted round shape. In some embodiments, a beanshape can have an inconsistent diameter. In some embodiments, a round orbean-shaped low density non-calcified plaque can be associated with ahigher risk of CAD or MI. In some embodiments, a lobular shape can be ashape comprised of four or more interconnected round and/or crescentshapes. In some embodiments, a lobular shape can be a narrow shapecomprised of interconnected round and/or crescent shapes. In someembodiments, a lobular shape can be a distorted crescent shape. In someembodiments, a lobular shaped low-density non-calcified plaque can beassociated with higher risk of CAD compared to a crescent shape butlower risk of CAD compared to a bean or round-shaped low densitynon-calcified plaque. In some embodiments, a low density non-calcifiedplaque can be classified as lobular v. bean-shape depending on thenumber of lobes. For example, in some embodiments, a low densitynon-calcified plaque with more lobes can be classified as lobular, whilea low density non-calcified plaque with fewer lobes can be classified asa bean-shape. FIG. 9A(e) illustrates examples of crescent, round,lobular, and/or bean-shaped regions of low density non-calcified plaque.

In some embodiments, the system can be configured to classify the shapeor morphology of a region of low density non-calcified plaqueautomatically or semi-automatically, for example using an imageprocessing algorithm. In some embodiments, the system can be configuredto classify the shape or morphology of a region of low densitynon-calcified plaque based at least in part on user input. In someembodiments, the system can be configured to classify the shape ormorphology of a region of low density non-calcified plaque based atleast in part on the one or more measures of one or more axes of the lowdensity non-calcified plaque. For example, in some embodiments, thesystem can be configured to compare the major longitudinal axis, majorlatitudinal axis, and/or minor latitudinal axis of a region of lowdensity non-calcified plaque in determining the shape of the low densitynon-calcified plaque. In some embodiments, the system can be configuredto take the standard deviation of one or more of the major longitudinalaxis, major latitudinal axis, and/or minor latitudinal axis, andclassify a low density non-calcified plaque as a round shape when thestandard deviation is below a certain predetermined threshold. In someembodiments, a crescent, lobular, and/or bean-shaped low densitynon-calcified plaque can be associated with a higher standard deviationamong axes measurements.

In some embodiments, the systems, devices, and methods can be configuredto determine a volume or size of total plaque, low density non-calcifiedplaque, non-calcified plaque, and/or calcified plaque, and/or a ratiothereof. In some embodiments, the volume or size of total plaque, lowdensity non-calcified plaque, non-calcified plaque, and/or calcifiedplaque can be a factor and/or indicator in determining the stability ofplaque and/or assessing risk of CAD or MI. For example, in someembodiments, a higher volume of total plaque, low density non-calcifiedplaque, and/or non-calcified plaque can be indicative of a high-riskplaque and/or high risk of CAD. In some embodiments, a high ratio of lowdensity non-calcified plaque to total plaque can be indicative of ahigh-risk plaque and/or high risk of CAD.

In some embodiments, the system can be configured to determine the sizeor volume of a region of low density non-calcified plaque automaticallyor semi-automatically, for example using an image processing algorithm.In some embodiments, the system can be configured to determine the sizeor volume of a region of low density non-calcified plaque based at leastin part on user input. In some embodiments, the system can be configuredto determine the size or volume of a region of low density non-calcifiedplaque based at least in part on the one or more measures of one or moreaxes of the low density non-calcified plaque.

Sample Study Regarding Plaque Morphology/Features Analysis

As discussed herein, in some embodiments, the systems, methods, anddevices are configured to analyze one or more plaque morphology and/orother features as an indicator and/or variable for determining risk ofCAD and/or MI. For example, in some embodiments, the systems, methods,and devices can be configured to analyze a medical image to derive oneor more plaque parameters and/or vascular parameters, such as totalplaque volume, low density non-calcified plaque volume, non-calcifiedplaque volume, calcified plaque volume, minimal lumen diameter, maximumremodeling index, maximum lesion length, and/or the like. In someembodiments, the systems, methods, and devices can be used to showand/or derive a correlation between one or such features and risk of CADand/or MI.

FIG. 9B illustrates example per-lesion high-risk plaque morphologycharacteristics derived from a sample study. As illustrated, FIG. 9Bsummarizes the frequency of the degree of embedded low-densitynon-calcified plaque (DELP) and low-density non-calcified (LDNC) depositshape in non-culprit and culprit lesions ((−) Culprit and (+) Culprit,respectively). In the illustrated sample study, 122 patients wereincluded, wherein the culprit lesion was adjudicated at the time of theacute coronary syndrome (ACS) event.

FIG. 9C illustrates example per-patient atherosclerotic characteristicsstratified by patients with and without an acute coronary syndrome eventderived from a sample study. In FIG. 9C, per-patient atheroscleroticcharacteristics are shown stratified by patients without and with anacute coronary syndrome event ((−) ACS and (+) ACS, respectively).

FIG. 9D illustrates example adjusted hazard ratios of the effect ofhigh-risk plaque morphology features on culprit lesion precursors toacute coronary syndrome derived from a sample study. In the examplestudy summarized in FIG. 9D, 122 patients with 965 coronary lesions wereassessed. In this study, all patients underwent culprit lesionadjudication at the time of acute coronary syndrome (ACS). In FIG. 9D, aforest plot of the hazard ratios for degree of embedded low-densitynon-calcified plaque (DELP) and low-density non-calcified plaque shape(crescent, bean, round, or lobular) is shown, on a per-lesion basis. Inthe study, high-risk plaque morphology (HRP Morphology) was defined asany lesion with a round or bean-shaped low-density non-calcifieddeposit, and DELP>180°. Additionally, in FIG. 9D, lesion features suchas the presence of low-density non-calcified plaque ((+) LDNC Plaque),positive remodeling ((+) Positive Remodeling), and two feature plaques((+) 2FPP) are shown for comparison. (+) 2FPP can refer to the presenceof both positive remodeling and any amount of low-density non-calcifiedplaque, for a given lesion. In the study, hazard ratios were adjustedfor angina and occurrence of revascularization. Also, in FIG. 9D,proportions of non-culprit ((−) Culprit Lesions) and culprit ((+)Culprit Lesions) are shown for each variable described in the forestplot.

FIG. 9E illustrates an example Kaplan-Meier curve of occurrence of acutecoronary syndrome in patients with and without a high-risk plaquemorphology lesion derived from a sample study. In FIG. 9E, patientswithout at least one high-risk plaque morphology lesion are shown ((−)HRP Morphology), and patients with at least one high-risk plaquemorphology lesion are shown ((+) HRP Morphology).

Plaque Morphology/Features Analysis—Additional Detail

FIG. 9F is a flowchart illustrating an example embodiment(s) of systems,devices, and methods for non-invasive image-based plaque analysis andrisk determination. As illustrated in FIG. 9F, in some embodiments, thesystem can be configured to access and/or modify one or more medicalimages at block 902. In some embodiments, the medical image can includeone or more arteries, such as coronary, carotid, and/or other arteriesof a subject. In some embodiments, the medical image can be stored in amedical image database 904. In some embodiments, the medical imagedatabase 904 can be locally accessible by the system and/or can belocated remotely and accessible through a network connection. Themedical image can comprise an image obtain using one or more modalitiessuch as for example, CT, Dual-Energy Computed Tomography (DECT),Spectral CT, photon-counting CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), Magnetic Resonance (MR) imaging,optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS). In someembodiments, the medical image comprises one or more of acontrast-enhanced CT image, non-contrast CT image, MR image, and/or animage obtained using any of the modalities described above.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 906, thesystem can be configured to identify one or more vessels, such as of oneor more arteries. The one or more arteries can include coronaryarteries, carotid arteries, aorta, renal artery, lower extremity artery,upper extremity artery, and/or cerebral artery, amongst others. In someembodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically identify one ormore arteries or coronary arteries using image processing. For example,in some embodiments, the one or more AI and/or ML algorithms can betrained using a Convolutional Neural Network (CNN) on a set of medicalimages on which arteries or coronary arteries have been identified,thereby allowing the AI and/or ML algorithm automatically identifyarteries or coronary arteries directly from a medical image. In someembodiments, the arteries or coronary arteries are identified by sizeand/or location.

In some embodiments, at block 908, the system can be configured toidentify one or more regions of plaque in the medical image. In someembodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically identify one ormore regions of plaque using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of (digital) medicalimages on which regions of plaque have been identified, thereby allowingthe AI and/or ML algorithm automatically identify regions of plaquedirectly from a medical image. In some embodiments, the system isconfigured to identify vessel and lumen walls and classify everything inbetween the vessel and lumen walls as plaque.

In some embodiments, at block 910, the system can be configured toanalyze and/or characterize one or more regions of plaque based ondensity. For example, in some embodiments, the system can be configuredto analyze and/or characterize one or more regions of plaque based onabsolute density and/or relative density and/or radiodensity. In someembodiments, the system can be configured to classify a region of plaqueas one of low density non-calcified plaque, non-calcified plaque, andcalcified plaque, using any one or more processes and/or featuresdescribed herein.

In some embodiments, at block 912, the system can be configured toanalyze and/or characterize one or more regions of plaque based on oneor more distances. For example, as described herein, in someembodiments, the system can be configured to determine a distancebetween a low density non-calcified plaque and lumen wall and/or vesselwall. In some embodiments, proximity of a low density non-calcifiedplaque to the lumen wall can be indicative of a high-risk plaque and/orCAD. Conversely, in some embodiments, a position of a low densitynon-calcified plaque far from the lumen wall can be indicative of lessrisk. In some embodiments, the system can be configured to utilize oneor more predetermined thresholds in determining the risk factorassociated with the proximity of low density non-calcified plaque withthe vessel wall and/or lumen wall. In some embodiments, the system canbe configured to utilize one or more image processing algorithms toautomatically and/or dynamically determine one or more distances toand/or from one or more regions of plaque.

In some embodiments, at block 914, the system can be configured toanalyze and/or characterize one or more regions of plaque based onmorphology or shape and/or one or more axes measurements of low densitynon-calcified plaque. As described herein, in some embodiments, thesystem can be configured to determine the length of one or more axes ofa low density non-calcified plaque, such as for example a major axis ofa longitudinal cross section and/or a major and/or minor axis of alatitudinal cross section of a low density non-calcified plaque. In someembodiments, the system can be configured to utilize the one more axesmeasurements to determine a morphology and/or shape of a low densitynon-calcified plaque. In some embodiments, the system can be configuredto utilize one or more image processing algorithms to automaticallyand/or dynamically determine one or more axes measurements of one ormore regions of plaque.

In some embodiments, the system can be configured to utilize one or moreAI and/or ML algorithms to automatically and/or dynamically classify theshape of one or more regions of plaque using image processing. Forexample, in some embodiments, the one or more AI and/or ML algorithmscan be trained using a Convolutional Neural Network (CNN) on a set ofmedical images on which the shape of regions of plaque have beenidentified, thereby allowing the AI and/or ML algorithm automaticallyidentify the shape or morphology of a region of plaque directly from amedical image. In some embodiments, the system can be configured toclassify the shape or morphology of a region of plaque as one or more ofcrescent, lobular, round, or bean-shaped. In some embodiments, roundand/or bean-shaped plaques can be associated with high risk, whilecrescent and/or lobular-shaped plaques can be associated with low riskof CAD.

In some embodiments, at block 916, the system can be configured toanalyze and/or characterize one or more regions of plaque based on oneor more sizes and/or volumes. For example, in some embodiments, thesystem can be configured to determine a size and/or volume of plaquebased at least in part on one or more axes measurements describedherein. In some embodiments, the system can be configured to determinethe size and/or volume of a region of plaque directly from analysis of athree-dimensional image scan. In some embodiments, the system can beconfigured to determine the size and/or volume of total plaque,low-density non-calcified plaque, non-calcified plaque, calcifiedplaque, and/or a ratio between two of the aforementioned volumes orsizes. In some embodiments, a high total plaque volume and/or highlow-density non-calcified plaque and/or non-calcified plaque volume canbe associated with high risk of CAD. In some embodiments, a high ratioof low-density non-calcified plaque volume to total plaque volume and/ora high ratio of non-calcified plaque volume to total plaque volume canbe associated with high risk of CAD. In some embodiments, a highcalcified plaque volume and/or high ratio of calcified plaque volume tototal plaque volume can be associated with low risk of CAD. In someembodiments, the system can be configured to utilize one or morepredetermined threshold values for determining the risk of CAD based onplaque volume, size, or one or more ratios thereof. In some embodiments,the system can be configured to utilize one or more image processingalgorithms to automatically and/or dynamically determine the size and/orvolume of one or more regions of plaque.

In some embodiments, at block 918, the system can be configured toanalyze and/or characterize plaque based on embeddedness. For example,in some embodiments, the system can be configured to determined howembedded or surrounded a low density non-calcified plaque is bynon-calcified plaque or calcified plaque. In some embodiments, thesystem can be configured to analyze the embeddedness of low densitynon-calcified plaque based on the degree by which it is surrounded byother types of plaque. In some embodiments, a higher embeddedness of alow density non-calcified plaque can be indicative of high risk of CAD.For example, in some embodiments, a low density non-calcified plaquethat is surrounded by 270 degrees or more by non-calcified plaque can beassociated with high risk of CAD. In some embodiments, the system can beconfigured to utilize one or more image processing algorithms toautomatically and/or dynamically determine the embeddedness of one ormore regions of plaque.

In some embodiments, at block 920, the system can be configured todetermine a risk of CAD or MI based on one or more plaque analysesdescribed herein, for example in relation to one or more of blocks902-918. In some embodiments, the system can be configured to utilizesome or all of the plaque analyses results. In some embodiments, thesystem can be configured to generate a weighted measure of some or allof the plaque analyses described herein in determining a risk of CAD. Insome embodiments, the system can be configured to refer to one or morereference values of one or more plaque analyses results in determiningrisk of CAD. For example, in some embodiments, the one or more referencevalues can comprise one or more values derived from a population withvarying states of risks of CAD, wherein the one or more values cancomprise one or more of one or more distances to and/or from a lowdensity non-calcified plaque, one or more axes measurements, morphologyclassification, size and/or volume, and/or embeddedness of low densitynon-calcified plaque. In some embodiments, the one or more referencevalues can be stored on a reference values database 922, which can belocally accessible by the system and/or can be located remotely andaccessible through a network connection.

In some embodiments, at block 924, the system can be configured togenerate a graphical representation of the analyses results, determinedrisk of CAD, and/or proposed treatment for the subject. In someembodiments, the analyses results can be displayed on a vessel, lesion,and/or subject basis. In some embodiments, the proposed treatment caninclude, for example, medical treatment such as statins, interventionaltreatment such as stent implantation, and/or lifestyle treatment such asexercise or diet. In some embodiments, in determining the risk or stateof cardiovascular disease or health and/or treatment, the system canaccess a plaque risk/treatment database 926, which can be locallyaccessible by the system and/or can be located remotely and accessiblethrough a network connection. In some embodiments, the plaquerisk/treatment database 926 can include reference points or data thatrelate one or more treatment to cardiovascular disease risk or statedetermined based on one or more reference plaque analyses values.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 902-926, for example for oneor more other vessels, segment, regions of plaque, different subjects,and/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease trackingand/or personalized treatment for a subject.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 9G. The example computer system 928 is incommunication with one or more computing systems 946 and/or one or moredata sources 948 via one or more networks 944. While FIG. 9G illustratesan embodiment of a computing system 928, it is recognized that thefunctionality provided for in the components and modules of computersystem 928 can be combined into fewer components and modules, or furtherseparated into additional components and modules.

The computer system 928 can comprise a Plaque Analysis Module 940 thatcarries out the functions, methods, acts, and/or processes describedherein. The Plaque Analysis Module 940 executed on the computer system928 by a central processing unit 306 discussed further below.

In general the word “module,” as used herein, refers to logic embodiedin hardware or firmware or to a collection of software instructions,having entry and exit points. Modules are written in a program language,such as JAVA, C, or C++, or the like. Software modules can be compiledor linked into an executable program, installed in a dynamic linklibrary, or can be written in an interpreted language such as BASIC,PERL, LAU, PHP or Python and any such languages. Software modules can becalled from other modules or from themselves, and/or can be invoked inresponse to detected events or interruptions. Modules implemented inhardware include connected logic units such as gates and flip-flops,and/or can include programmable units, such as programmable gate arraysor processors.

Generally, the modules described herein refer to logical modules thatcan be combined with other modules or divided into sub-modules despitetheir physical organization or storage. The modules are executed by oneor more computing systems, and can be stored on or within any suitablecomputer readable medium, or implemented in-whole or in-part withinspecial designed hardware or firmware. Not all calculations, analysis,and/or optimization require the use of computer systems, though any ofthe above-described methods, calculations, processes, or analyses can befacilitated through the use of computers. Further, in some embodiments,process blocks described herein can be altered, rearranged, combined,and/or omitted.

The computer system 928 includes one or more processing units (CPU) 932,which can comprise a microprocessor. The computer system 928 furtherincludes a physical memory 936, such as random access memory (RAM) fortemporary storage of information, a read only memory (ROM) for permanentstorage of information, and a mass storage device 930, such as a backingstore, hard drive, rotating magnetic disks, solid state disks (SSD),flash memory, phase-change memory (PCM), 3D XPoint memory, diskette, oroptical media storage device. Alternatively, the mass storage device canbe implemented in an array of servers. Typically, the components of thecomputer system 928 are connected to the computer using a standardsbased bus system. The bus system can be implemented using variousprotocols, such as Peripheral Component Interconnect (PCI), MicroChannel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA(EISA) architectures.

The computer system 928 includes one or more input/output (I/O) devicesand interfaces 938, such as a keyboard, mouse, touch pad, and printer.The I/O devices and interfaces 938 can include one or more displaydevices, such as a monitor, which allows the visual presentation of datato a user. More particularly, a display device provides for thepresentation of GUIs as application software data, and multi-mediapresentations, for example. The I/O devices and interfaces 938 can alsoprovide a communications interface to various external devices. Thecomputer system 928 can comprise one or more multi-media devices 934,such as speakers, video cards, graphics accelerators, and microphones,for example.

Computing System Device/Operating System

The computer system 928 can run on a variety of computing devices, suchas a server, a Windows server, a Structure Query Language server, a UnixServer, a personal computer, a laptop computer, and so forth. In otherembodiments, the computer system 928 can run on a cluster computersystem, a mainframe computer system and/or other computing systemsuitable for controlling and/or communicating with large databases,performing high volume transaction processing, and generating reportsfrom large databases. The computing system 928 is generally controlledand coordinated by an operating system software, such as z/OS, Windows,Linux, UNIX, BSD, PHP, SunOS, Solaris, MacOS, ICloud services or othercompatible operating systems, including proprietary operating systems.Operating systems control and schedule computer processes for execution,perform memory management, provide file system, networking, and I/Oservices, and provide a user interface, such as a graphical userinterface (GUI), among other things.

Network

The computer system 928 illustrated in FIG. 9G is coupled to a network944, such as a LAN, WAN, or the Internet via a communication link 942(wired, wireless, or a combination thereof). Network 944 communicateswith various computing devices and/or other electronic devices. Network944 is communicating with one or more computing systems 946 and one ormore data sources 948. The Plaque Analysis Module 914 can access or canbe accessed by computing systems 946 and/or data sources 948 through aweb-enabled user access point. Connections can be a direct physicalconnection, a virtual connection, and other connection type. Theweb-enabled user access point can comprise a browser module that usestext, graphics, audio, video, and other media to present data and toallow interaction with data via the network 944.

The output module can be implemented as a combination of an all-pointsaddressable display such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a plasma display, or other types and/or combinations ofdisplays. The output module can be implemented to communicate with inputdevices 938 and they also include software with the appropriateinterfaces which allow a user to access data through the use of stylizedscreen elements, such as menus, windows, dialogue boxes, tool bars, andcontrols (for example, radio buttons, check boxes, sliding scales, andso forth). Furthermore, the output module can communicate with a set ofinput and output devices to receive signals from the user.

Other Systems

The computing system 928 can include one or more internal and/orexternal data sources (for example, data sources 948). In someembodiments, one or more of the data repositories and the data sourcesdescribed above can be implemented using a relational database, such asDB2, Sybase, Oracle, CodeBase, and Microsoft® SQL Server as well asother types of databases such as a flat-file database, an entityrelationship database, and object-oriented database, and/or arecord-based database.

The computer system 928 can also access one or more databases 948. Thedatabases 948 can be stored in a database or data repository. Thecomputer system 928 can access the one or more databases 948 through anetwork 944 or can directly access the database or data repositorythrough I/O devices and interfaces 938. The data repository storing theone or more databases 948 can reside within the computer system 928.

URLs and Cookies

In some embodiments including any of the embodiments disclosed herein(above or below) one or more features of the systems, methods, anddevices described herein can utilize a URL and/or cookies, for examplefor storing and/or transmitting data or user information. A UniformResource Locator (URL) can include a web address and/or a reference to aweb resource that is stored on a database and/or a server. The URL canspecify the location of the resource on a computer and/or a computernetwork. The URL can include a mechanism to retrieve the networkresource. The source of the network resource can receive a URL, identifythe location of the web resource, and transmit the web resource back tothe requestor. A URL can be converted to an IP address, and a DomainName System (DNS) can look up the URL and its corresponding IP address.URLs can be references to web pages, file transfers, emails, databaseaccesses, and other applications. The URLs can include a sequence ofcharacters that identify a path, domain name, a file extension, a hostname, a query, a fragment, scheme, a protocol identifier, a port number,a username, a password, a flag, an object, a resource name and/or thelike. The systems disclosed herein can generate, receive, transmit,apply, parse, serialize, render, and/or perform an action on a URL.

A cookie, also referred to as an HTTP cookie, a web cookie, an internetcookie, and a browser cookie, can include data sent from a websiteand/or stored on a user's computer. This data can be stored by a user'sweb browser while the user is browsing. The cookies can include usefulinformation for websites to remember prior browsing information, such asa shopping cart on an online store, clicking of buttons, logininformation, and/or records of web pages or network resources visited inthe past. Cookies can also include information that the user enters,such as names, addresses, passwords, credit card information, etc.Cookies can also perform computer functions. For example, authenticationcookies can be used by applications (for example, a web browser) toidentify whether the user is already logged in (for example, to a website). The cookie data can be encrypted to provide security for theconsumer. Tracking cookies can be used to compile historical browsinghistories of individuals. Systems disclosed herein can generate and usecookies to access data of an individual. Systems can also generate anduse JSON web tokens to store authenticity information, HTTPauthentication as authentication protocols, IP addresses to tracksession or identity information, URLs, and the like.

Plaque Morphology/Features Analysis—Additional Study Details

In some embodiments, the systems, methods, and devices described hereinare configured to assess plaque morphology non-invasively, for exampleusing coronary CT angiography (CCTA), which can be used to improve thestratification of patients at risk for acute coronary syndrome (ACS).More specifically, in some embodiments, one or more low-densitynon-calcified plaque (LD-NCP) morphologies described herein can beidentified using one or more technologies, such as for exampleartificial intelligence-enabled quantitative CT (AI-QCT). In someembodiments, the association between such one or more low-densitynon-calcified plaque (LD-NCP) morphologies with ACS can be compared to,confirmed, and/or strengthened by other high-risk plaque (HRP) features,such as for example napkin ring sign (NRS).

In an example study to confirm the association of such LD-NCPmorphologies with ACS, 456 patients from the ICONIC study were included,where all patients underwent CCTA and follow-up for ACS. In the examplestudy, during the follow-up period, ACS occurred in 226 patients. In theexample study, using invasive coronary angiography, culprit lesions wereadjudicated to CCTA images in 122 patients at the time of ACS. Further,in the example study, LD-NCP morphology was assessed based on shape andthe degree of embedded LD-NCP plaque (DELP) following retrospectiveanalysis of CCTA images with AI-QCT.

In the example study, high-risk plaque (HRP) morphology was defined as alesion with at least one round or bean-shaped LD-NCP deposit with ≥270°degree of embedded low-density non-calcified plaque (DELP). In theexample study, it was shown that HRP morphology was strongly associatedwith culprit lesion precursors (aHR (adjusted Hazard Ratio): 8.253,[4.785,14.234], p-value<0.001). In the example study, HRP morphologyalso demonstrated a strong association with ACS (aHR: 2.484,[1.778,3.470], p-value<0.001). In the example study, HRP morphology wasdetected in 40 (17.7%) of ACS patients and NRS was detected in 12(5.3%). In the example study, the median time to ACS in patients withoutand with HRP morphology was 1845 days and 40 days, respectively(p-value<0.001).

As such, in the example study, it was shown that HRP morphology has asignificant association with ACS and culprit lesion precursors anddemonstrated a stronger predictor than other HRP features.

As discussed herein, identifying patients at high risk for acutecoronary syndrome (ACS) remains one of the most challenging tasks incurrent cardiovascular disease prevention practice. Despite theabundance of novel ASCVD risk lowering strategies, ACS rates in bothprimary as well as secondary prevention remain high. Coronary CTangiography (CCTA) imaging can provide non-invasive assessment ofatherosclerotic plaque characteristics, as well as coronary arterystenosis. While the presence of obstructive coronary artery disease(CAD) can provide prognostic value, most culprit lesion precursors toACS can be non-obstructive. As such, in some embodiments, the system canbe configured analyze, identify, and/or utilize the presence of one ormore qualitative high-risk plaque (HRP) features, such aslow-attenuation plaque, positive remodeling, spotty calcifications, orthe napkin-ring sign, as indicators of increased risk for ACS. Of note,in some embodiments, the system can be configured to weight obstructivelesions and non-obstructive HRP lesions the same or similarly for riskfor ACS.

In some embodiments, the system can be configured to weigh thenapkin-ring sign more heavily than other indicators of HRP, as thenapkin-ring sign has shown high correspondence with histologic findingsof thin fibrous atheroma caps and plaque rupture. More specifically, thenapkin ring sign can be a morphologic CCTA finding, wherein low-densitynon-calcified plaque (LD-NCP) is completely circumscribed byhigher-attenuating plaque. However, the clinical value of this featuremay be somewhat limited due to its low prevalence and modestsensitivity.

In the example study described herein, the association of LD-NCP shapeand the degree of embedded LD-NCP with culprit lesions and ACS wasevaluated and demonstrated. For this purpose, in the example study, CCTAimages from the multicenter ICONIC study were retrospectively analyzedusing an automated artificial intelligence (AI)-enabled web-basedsoftware platform. In the example study, after AI-based plaquecharacterization, LD-NCP morphology was assessed based on shape, e.g.,round, bean-shaped, crescent, or lobular, and the degree of embeddedLD-NCP (DELP). Additionally, in the example study, the prognostic valueof atherosclerotic plaque characteristics measured using AI-based plaquequantification was evaluated. As such, by utilizing identificationand/or categorization of LD-NCP morphology based on shape and DELP, someembodiments of the systems, devices, and methods described herein can beused to improve the stratification of patients for risk of ACS.

In the example study utilizing data from the multicenter, internationalICONIC (Incident Coronary Syndromes Identified by Computed Tomography)study (NCT02959099), 234 patients who underwent coronary CT angiography(CCTA) prior to acute coronary syndrome (ACS) during follow-up were 1:1matched to non-ACS controls that underwent CCTA. In the example study,case and control patients underwent propensity score matching based onclinical risk factors for CAD, including age, male sex, history ofhypertension and hyperlipidemia, family history of premature CAD, andsmoking, as well as coronary stenosis severity as assessed on CCTA, allof which can be used as factors and/or indicators of CAD or ACS in someembodiments of the systems, devices, and methods described herein. Inthe example study, the culprit coronary lesion was adjudicated to CCTAimages at the time of ACS, using invasive coronary angiography (ICA). Inthe example study, culprit lesions could not be adjudicated in 104patients with ACS due to no ICA being performed, missing ICA images, orno culprit lesion identified on ICA. In the example study, all CCTAimaging was performed with ≥64 detector row systems and in accordancewith SCCT guidelines at the time. In the example study, CCTA imagesunderwent artificial-intelligence enabled quantitative CT (AI-QCT)analysis, similar to some embodiments of systems, devices, and methodsdescribed herein. In the example study, the associations between LD-NCPmorphology and culprit lesion precursors, as well as ACS were assessed.

In the example study, and in some embodiments described herein,quantitative CT analysis was performed. More specifically, in theexample study, and in some embodiments of systems, devices, and methodsherein, coronary vessels with a diameter≥1.5 mm were identified,analyzed and segmented based on the 18-segment SCCT model. In theexample study, and in some embodiments herein, the presence ofatherosclerosis was defined as any tissue structure>1 mm² within thecoronary artery wall, differentiated from epicardial fat, epicardialtissue, or the coronary lumen. In the example study, and in someembodiments herein, coronary atherosclerotic plaque was characterizedusing an automated artificial intelligence (AI)-enabled web-basedsoftware platform. In the example study, and in some embodiments herein,using Hounsfield unit (HU) ranges, plaque volume was categorized asLD-NCP (<30 HU), non-calcified (30-350 HU), and calcified (>350 HU). Inthe example study, and in some embodiments herein, percent atheromavolume was calculated as total plaque volume/coronary vesselvolume×100%. In the example study, and in some embodiments herein,percent LD-NCP, non-calcified, and calcified plaque was calculated asthe specific plaque volume/total plaque volume. In the example study,and in some embodiments herein, diameter stenosis was measured using anAI-enabled quantitative CT method (QCT).

In the example study, and in some embodiments herein, LD-NCP morphologywas evaluated. In particular, in the example study, and in someembodiments herein, the morphology of noncontiguous LD-NCP deposits werequalitatively assessed by a level-III qualified CCTA reader (JPE) withconsensus agreement with an additional level-III qualified reader (ADC).In the example study, and in some embodiments herein, up to 4 LD-NCPdeposits were assessed per coronary plaque. In the example study, and insome embodiments herein, LD-NCP deposits with a maximum axis measurementless than 0.5 mm were ignored, and plaques with less than 0.5 mm3 ofLD-NCP were not assessed. In the example study, and in some embodimentsherein, the analysis was performed using straightened multiplanarreformatted (SMPR) images with color plaque overlay tool turned on asavailable in AI-QCT.

In the example study, and in some embodiments herein, LD-NCP depositshape was categorized as crescent, lobular, bean-shaped, and/or round,based on its appearance on cross-sectional and longitudinal SMPR images.FIG. 9H illustrates an example embodiment(s) of assessing morphology oflow-density non-calcified plaque deposits. In the example study, and insome embodiments herein, the degree of embedded LD-NCP (DELP) wasassessed, based on the extent a LD-NCP deposit was encircled bynon-calcified or calcified plaque. In the example study, and in someembodiments herein, using the cross-sectional image with the greatestLD-NCP deposit area, per-plaque DELP was estimated as <180°, 180-269°,270-359°, or 360°. In the example study, and in some embodiments herein,high-risk plaque (HRP) morphology was defined as a round or bean-shapedLD-NCP deposit with ≥270° DELP.

In the example study, patient demographic variables were comparedbetween patients without and with occurrence of ACS, using a two-samplet-test for continuous variables, or the chi-squared or Fisher's exacttest for categorical variables, depending on sample size. In the examplestudy, and in some embodiments herein, LD-NCP deposit measurements wereassessed in association with ACS.

In the example study, the robust variance estimator and marginal Coxregression analysis was used to adjust for patient clustering. In theexample study, on a per-patient basis, HRP morphology andatherosclerotic plaque characteristics were assessed in association withoccurrence of ACS using marginal Cox regression analysis. In the examplestudy, on a per-lesion basis, HRP morphology and atherosclerotic plaquecharacteristics were assessed in association with culprit lesionprecursors. In the example study, for per-lesion assessment, culpritlesions were compared to all non-culprit lesions within-patient, inpatients with ACS and culprit lesion adjudication.

In the example study, event-free survival curve analysis was conductedon a per-patient basis, stratified by patients with at least one HRPmorphology lesion, as well as based on the amount of maximum LD-NCPvolume in a lesion. In the example study, the Gehan-Breslow test wasused to determine statistical significance. Additionally, in the examplestudy, the HRP definition of presence of a lesion with two or more HRPs:low-attenuation plaque, spotty calcifications, or remodeling index≥1.1was used to compare the morphology-based HRP described herein.

In the example study, hazard ratios were calculated using time to firstACS event, or event-free follow-up date. In the example study, allhazard ratios were adjusted for interval revascularization and anginaseverity. In the example study, a p-value<0.05 was consideredstatistically significant for all analyses conducted for this study. Inthe example study, continuous variables were represented as MEAN±SD,while categorical variables were shown as number (percentage). In theexample study, hazard ratios were represented as HR [95% confidenceinterval]. In the example study, all statistical analysis was performedusing R 4.2.0.

In the example study, 456/468 patients (97.5%) from the ICONIC study,including 122 patients with culprit lesion adjudication, were included.In the example study, ten patients were excluded due to missing CCTAimages, and two patients were excluded due to poor image quality. FIG.9I is a flowchart illustrating analyses of the example study datasetvalidating some embodiments of the systems, devices, and methods herein.As illustrate in FIG. 9I, in the example study, high-risk plaque (HRP)morphology was retrospectively analyzed using data from the ICONICstudy. In the example study, coronary CT angiography (CCTA) analysis wasrepeated using an artificial-intelligence enabled quantitative CTanalysis (AI-QCT), using the original images from the ICONIC study. Inthe example study, of the 468 patients in ICONIC, 456 were included inthis post-hoc analysis. In the example study, the association of HRPmorphology with acute coronary syndrome was assessed on a per-patientbasis. In the example study, the association of HRP morphology withculprit lesion precursors was assessed in 122 patients. In the examplestudy, all 122 patients underwent culprit lesion adjudication at thetime of the ACS event. FIG. 9J illustrates patient demographicsstratified by occurrence of acute coronary syndrome in the example studyvalidating some embodiments of systems, methods, and devices describedherein. As illustrated in FIG. 9J, in the example study, 456 patientsincluded a mean follow-up time of 2.71±2.74 years for ACS following CCTAimaging.

As for per-lesion results, in the example study, atherosclerotic plaquecharacteristics were evaluated in 965 lesions of 122 patients with ACSand culprit lesion adjudication. FIG. 9K illustrates per-lesionatherosclerotic characteristics stratified by non-culprit and culpritlesion precursors in the example study validating some embodiments ofsystems, methods, and devices described herein. As illustrated in FIG.9K, in the example study, culprit lesions had a significantly higherplaque of the various atherosclerotic plaque characteristics includingtotal plaque volume, non-calcified plaque volume and LD-NCP volume.

FIG. 9L is a forest plot of hazard ratios of risk for culprit lesionprecursors in the example study validating some embodiments of systems,methods, and devices described herein. As illustrated in FIG. 9L, in theexample study, culprit lesions were identified in 122 patients at thetime of an acute coronary syndrome event, using invasive coronaryangiography. In the example study, all lesions were adjudicated to aculprit lesion precursor on a previously acquired coronary CTangiography (CCTA) image. In the example study, and in some embodimentsdescribed herein, high-risk plaque (HRP) morphology was defined as anylesion with at least one low-density non-calcified plaque (LD-NCP)deposit that was round or bean-shaped with ≥270° degree of embeddedLD-NCP (DELP). In the example study, and in some embodiments describedherein, diameter stenosis, measured using artificial intelligencequantitative CT (DS_(AI-QCT)) was assessed for association with culpritlesion precursors. In the example study, and in some embodimentsdescribed herein, lesions with a remodeling index≥1.1 with LD-NCPpresent were assessed for association with culprit lesion precursors. Inthe example study, there were 122 culprit lesion precursors and 843non-culprit lesion precursors. In FIG. 9L, a forest plot and bar graphof the frequency of each variable is shown as a percentage ofnon-culprit (darker) and culprit lesion precursors (lighter).

As illustrated in FIGS. 9K and 9L, in the example study, 19 (15.57%) ofculprit lesions had HRP morphology, e.g., lesions with at least oneround or bean-shaped LD-NCP deposit with ≥270° DELP, (adjusted hazardratio (aHR):8.253 [4.785, 14.234]; p-value<0.001). In the example study,per-lesion total plaque volume was 35.21±87.42 mm3 and 154.99±188.37 mm3for non-culprit and culprit lesions, respectively (aHR: 1.003 [1.003,1.004]; p-value<0.001). In the example study, LD-NCP volume was0.22±2.20 mm3 and 1.99±6.03 mm3 for non-culprit and culprit lesions,respectively (aHR: 1.037 [1.003, 1.071]; p-value=0.032). In the examplestudy, DS_(AI-QCT) was 13.65±15.32% and 33.61±22.11% in non-culprit andculprit lesions (aHR: 1.048 [1.040, 1.055]; p-value<0.001).

As for per-patient results, in the example study, atherosclerotic plaquecharacteristics from CCTA were evaluated from 456 patients. FIG. 9Millustrates per-patient atherosclerotic characteristics stratified bypatients with and without occurrence of acute coronary syndrome in theexample study validating some embodiments of systems, methods, anddevices described herein. As illustrated in FIG. 9M, in the examplestudy, atherosclerotic characteristics were described for 456 patients,230 patients without occurrence of acute coronary syndrome (ACS) and 226patients with ACS ((−) ACS and (+) ACS, respectively). In other words,in the example study, 40/47 (85.11%) patients with HRP morphology hadACS during follow-up (aHR: 2.484 [1.778, 3.470]; p-value<0.001). In theexample study, per-patient total plaque volume was 309.48 (333.57) mm³and 359.63 (330.45) mm³ for patients without and with ACS, respectively(aHR: 1.000 [1.000, 1.001]; p-value=0.336). In the example study, totalLD-NCP volume was 0.82±2.71 mm³ and 2.59±7.88 mm³ for patients withoutand with ACS, respectively (aHR: 1.028 [1.013, 1.044]; p-value<0.001).In the example study, max DS_(AI-QCT) was 34.26±20.93% and 41.25±21.97%in patients without and with ACS, respectively (aHR: 1.007 [1.001,1.014]; p-value=0.032).

In the example study, the adjusted hazard ratio was significantly higherwith the HRP morphology definition compared to the HRP definition basedon presence of a lesion with two or more HRPs: low-attenuation plaque,spotty calcifications, or remodeling index≥1.1 (aHR: 1.638 [1.241,2.486] vs 2.484 [1.778, 3.470]; p=0.022). FIG. 9N is a forest plot ofhazard ratios of association with acute coronary syndrome derived fromthe example study validating some embodiments of systems, methods, anddevices described herein. As illustrated in FIG. 9N, in the examplestudy, hazard ratios were calculated from 226 patients who had acutecoronary syndrome (ACS), and 230 matched patients without ACS, followingcoronary CT angiography imaging. As illustrated in FIG. 9N, the exampleforest plot demonstrates the association of several variables measuredfrom CCTA. In the example study, high-risk plaque (HRP) morphology,diameter stenosis (DS_(AI-QCT))≥70% and DS_(AI-QCT)≥50% were assessed ina post-hoc analysis of the ICONIC dataset (red). In the example study,and in some embodiments described herein, HRP defined as the presence oftwo or more of spotty calcification, remodeling index≥1.1, or presenceof low attenuation plaque (HRP_(ICONIC)), presence of a calcification≤3mm or 5 cross-sectional slices (Spotty Calcifications_(ICONIC)),Presence of remodeling index≥1.1 (Positive Remodeling_(ICONIC)),Presence of low attenuation plaque (<30 HU) (Low AttenuationPlaque_(ICONIC)), and Napkin Ring Sign_(ICONIC) were assessed. Further,as illustrated in FIG. 9N, a bar graph derived from the example studydemonstrates the frequency and portion of patients with each respectivevariable, stratified by patients without and with ACS (darker andlighter, respectively).

In the example study, and in some embodiments described herein, an acutecoronary syndrome event-free survival analysis was performed. In theexample study, HRP morphology was detected in 40 (17.7%) of patientsexperiencing ACS and 7 (3.0%) of those not experiencing ACS; a napkinring sign (NRS) was detected in 12 (5.3%) of ACS patients and in 8(3.5%) of non-ACS patients. In the example study, the median time to ACSin patients without and with HRP morphology was 5.06 and 0.11 years,respectively. FIG. 9O illustrates acute coronary syndrome event-freesurvival rate in patients without and with high-risk plaque morphologylesions in the example study validating some embodiments of systems,methods, and devices described herein. In FIG. 9O, patients with atleast one lesion with high-risk plaque morphology (e.g., round orbean-shaped low-density non-calcified plaque (LD-NCP) deposit with ≥270°degree of embedded LD-NCP) are shown ((+) HRP Morphology). In FIG. 90,patients without at least one HRP morphology lesion are shown((−) HRPMorphology). In the example study, the Gehan-Breslow test was used tocompare patient groups. In the example study, overall, 230 patients didnot have an ACS event during follow-up, whereas 226 patients did. In theexample study, forty-seven patients had at least one HRP morphologylesion, forty of these patients had an ACS event.

In the example study, and in some embodiments described herein, anincremental association of low-density non-calcified plaque volume withacute coronary syndrome was found on a per-patient basis. In particular,in the example study, patients with a maximum LD-NCP lesion volume≥2 mm³were strongly associated with ACS (aHR: 1.767 [1.285, 2.430];p-value<0.001)). In the example study, patients with a maximum LD-NCPlesion volume>0-1 mm³ were not associated with ACS (aHR: 1.036 [0.734,1.464]; p-value=0.840)). FIG. 9P is a forest plot of hazard ratios ofassociation with low-density non-calcified plaque with acute coronarysyndrome derived from an example study validating some embodiments ofsystems, methods, and devices described herein. In the example study,hazard ratios were calculated from 226 patients who had acute coronarysyndrome (ACS), and 230 matched patients without ACS, following coronaryCT angiography imaging In FIG. 9P, the incremental association of thelesion with the maximum low-density non-calcified plaque (LD-NCP) volumeper-patient is shown. In the example study, patients were stratifiedbased on the presence of at least one lesion with 0, >0-2, and ≥2 mm3LD-NCP volume. In FIG. 9P, a forest plot is shown with adjusted hazardrations and 95% confidence intervals, and the bar graph demonstrates thefrequency and portion of patients with each respective variable,stratified by patients without and with ACS (darker and lighter,respectively).

FIG. 9Q illustrates acute coronary syndrome event-free survival rates inpatients stratified by the amount of low-density non-calcified plaquevolume derived from an example study validating some embodiments ofsystems, methods, and devices described herein. In the example study,and in some embodiments described herein, patients were stratified into0, >0-2, and >2 mm³ low-density non-calcified plaque (LD-NCP) groups,based the lesion with the maximum volume of LD-NCP. In the examplestudy, the Gehan-Breslow test was used to compare patient stratum withthe p-value annotated. As illustrated in FIG. 9Q, in the example study,the median survival times of patients with a maximum LD-NCP lesionvolume of 0 mm³, ≥0-2 mm³, and >2 mm³ were 5.80, 3.15, and 0.86 years,respectively.

In the example study, and in some embodiments described herein, HRPdefined by one or more features and/or characteristics related tomorphology was used and proved superior to both the diameter stenosisanalysis, as well as an HRP definition based on two-feature positiveplaque in distinguishing ACS patients and matched controls. Asdiscussed, in the example study, and in some embodiments describedherein, HRP morphology was strongly associated with early ACSoccurrence, based on median time to ACS. Furthermore, in the examplestudy and in some embodiments described herein, on a per-lesion basis,culprit lesion precursors had greater total plaque volume, lesionlength, and/or LD-NCP, non-calcified, and/or calcified plaque volumes.In the example study and in some embodiments described herein, on aper-patient basis, ACS patients had greater LD-NCP and non-calcifiedplaque volume. Collectively, this example study demonstrated theclinical value of HRP morphology, as well as the effectiveness of anautomated AI-based method for measuring atherosclerotic plaquecharacteristics as used in some embodiments described herein. Moreover,in the example study and in some embodiments discussed herein, LD-NCPvolume specifically demonstrated increased association with ACS, inpatients with a maximum LD-NCP lesion volume≥2.0 mm³, as compared topatients with a maximum LD-NCP lesion volume<2.0 mm³.

In some embodiments, and as noted in the example study, thepathophysiology of HRP morphology can be important and/or be used as anindicator and/or characteristic of HRP. In particular, in the examplestudy and in some embodiments, crescent and lobular shaped LD-NCP, andplaques with <270° DELP demonstrated a lower risk for ACS and were lessfrequent culprit lesion precursors. In the example study and in someembodiments described herein, Crescentic and lobular LD-NCP depositsalso frequently demonstrated<270° DELP. As such, in some embodiments, aplaque region comprising a bean or round shape with ≥270° DELP can beidentified as HRP and/or can be associated with ACS. In someembodiments, a plaque region comprising a bean or round shape alone canbe identified as HRP and/or can be associated with ACS. In someembodiments, a plaque region comprising ≥270° DELP can be identified asHRP and/or can be associated with ACS. In some embodiments, and in theexample study, LD-NCP deposits with <270° DELP can have a greaterintersection with the outer coronary wall. In some embodiments, althoughplaque instability and rupture is related to the presence of a thincapped fibroatheroma(20), this can be difficult to assess on CCTA due tospatial resolution limitations. In some embodiments, DELP can beconsidered be a surrogate for the presence of a thin cappedfibroatheroma, wherein LD-NCP with a smaller DELP can be located moretowards the epicardial coronary vessel wall. Thus, in some embodiments,a smaller DELP implies a greater distance between LD-NCP and thecoronary lumen surface, and a lower risk for rupture. Furthermore, insome embodiments, patients with HRP morphology can be precited toexperience ACS significantly sooner, implying LD-NCP deposits with ≥270°DELP may be more prone to rupture.

In some embodiments, a napkin-ring sign (NRS) can be considered anindicator and/or characteristic of HRP. In some embodiments, a region ofplaque comprising an NRS can comprise histologic correspondence toadvance plaque and thin capped fibroatheromas. In some embodiments, NRSis considered to have a strong association with ACS. In someembodiments, HRP morphology features discussed herein can share somesimilarities with NRS; however, the low prevalence of NRS can limit itsclinical utility. In some embodiments, HRP morphology can provide abroader stratum for identifying significant atherosclerosis. Forexample, in the example study, NRS was shown to have a lower prevalencein comparison to HRP morphology on a per-patient basis.

In some embodiments, the system is configured to identify two-featurepositive plaques as HRP. That is, in some embodiments, the system isconfigured to identify as HRP a region of plaque with a presence of bothpositive remodeling and low-attenuation plaque. In some embodiments,two-feature positive plaques are considered to be associated withincreased risk for ACS.

In the example study, a direct comparison was tested between two-featurepositive plaque and HRP based on one or more morphology features, inassociation with culprit lesion precursors. In the example study, inboth outcomes, HRP based on one or more morphology features demonstrateda stronger association, with far less false positive cases, with ACS.

In some embodiments, the presence of one or more HRP features, such aslow-attenuation plaque (e.g., plaque<30 HU), positive remodeling, spottycalcifications, and/or NRS is considered to have a strong associationwith adverse cardiac events. Furthermore, in some embodiments, lesionswith HRP are considered more likely to be culprit lesion precursors. Inthe example study, a direct comparison showed that patients with HRPidentified based on one or more morphology features were at greater riskof ACS, as compared to one or more other HRP features.

In some embodiments, the system can be configured to utilize radiomicsand CCTA imaging to derive and/or identify geometry features of LD-NCP,which can be used to identify high-risk coronary lesions.

As shown in the example study, the median time to ACS in patients withHRP identified based on one or more morphology features was remarkablyreduced compared to HRP identified based on one or more other HRPfeatures. As such, in some embodiments, the system can be configured toutilize HRP identified based on one or more morphology features tostratify patients for high-intensity medical therapies. In someembodiments, the presence of LD-NCP alone is not used to stratifypatients for risk of ACS, but rather HRP morphology features, and/orlesion volume of LD-NCP, can be considered to adequately infer risk ofACS.

As such, in some embodiments, high-risk plaque morphology, based onlow-density non-calcified plaque for example, can be used to identifypatients at very high risk for acute coronary syndrome. Moreover, insome embodiments, patients with a lesion with at least 2 mm³ oflow-density non-calcified plaque volume can be considered to be stronglyassociated with acute coronary syndrome. Thus, in some embodiments, theamount and morphology of low-density non-calcified plaque can beconsidered when stratifying patients for risk of acute coronarysyndrome.

FIG. 9R illustrates how high-risk plaque morphology can be considered toincrease risk for acute coronary syndrome in some embodiments. Asillustrated, in some embodiments, one or more high-risk plaquemorphologies discussed herein can be considered to be stronglyassociated with acute coronary syndrome and/or culprit lesionprecursors. In particular, in some embodiments, crescentic low-densitynon-calcified plaque can be considered not to be associated with riskfor acute coronary syndrome, whereas round or bean-shaped low-densitynon-calcified plaque can be considered to have greater association withacute coronary syndrome. In some embodiments, artificialintelligence-based quantitative coronary CT angiography can be used tostratify patients for risk of acute coronary syndrome, for example basedon one or more morphology features described herein. Further, in someembodiments, the amount of low-density non-calcified plaque can beconsidered to be associated with increased risk for acute coronarysyndrome.

Certain Examples of Embodiments Related to Plaque Morphology/FeatureAnalysis

The following are non-limiting examples of certain embodiments ofsystems and methods for determining plaque morphology and/or featureanalysis. Other embodiments may include one or more other features, ordifferent features, that are discussed herein.

Embodiment 1: A computer-implemented method of facilitatingdetermination of risk of coronary artery disease (CAD) based at least inpart on one or more measurements derived from non-invasive medical imageanalysis, the method comprising: accessing, by a computer system, amedical image of a subject, wherein the medical image of the subject isobtained non-invasively; analyzing, by the computer system, the medicalimage of the subject to identify one or more arteries; identifying, bythe computer system, one or more regions of plaque within the one ormore coronary arteries; analyzing, by the computer system, theidentified one or more regions of plaque to identify one or more regionsof low density non-calcified plaque, non-calcified plaque, or calcifiedplaque based at least in part on density, analyzing, in response toidentifying one or more regions of low density non-calcified plaque, theone or more regions of low density non-calcified plaque, wherein theanalysis of the one or more regions of low density non-calcified plaquecomprises: determining a distance from the one or more regions of lowdensity non-calcified plaque to one or more of a lumen wall or vesselwall; determining a degree of embeddedness of the one or more regions oflow density non-calcified plaque by one or more of non-calcified plaqueor calcified plaque; and determining a shape of the one or more regionsof low density non-calcified plaque; and generating, by the computersystem, a display of the analysis of the one or more regions of lowdensity non-calcified plaque to facilitate determination of one or moreof a risk of CAD of the subject based at least in part on the analysisof the one or more regions of low density non-calcified plaque, whereinthe computer system comprises a computer processor and an electronicstorage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, wherein adetermination of the distance from the one or more regions of lowdensity non-calcified plaque to the lumen wall below a predeterminedthreshold is indicative of an unstable plaque or high risk of CAD.

Embodiment 3: The computer-implemented method of Embodiment 1, whereinthe distance from the one or more regions of low density non-calcifiedplaque to one or more of the lumen wall or vessel wall is determined ona three-dimensional basis.

Embodiment 4: The computer-implemented method of Embodiment 1, whereinthe distance from the one or more regions of low density non-calcifiedplaque to one or more of the lumen wall or vessel wall is determinedbased on a two-dimensional image.

Embodiment 5: The computer-implemented method of Embodiment 4, whereinthe two-dimensional image is obtained by taking a two-dimensional sliceperpendicular to a longitudinal axis of a straightened multiplanar viewof the one or more arteries.

Embodiment 6: The computer-implemented method of Embodiment 4, whereinthe two-dimensional image is obtained by taking a two-dimensional sliceresulting in a largest two-dimensional area of the low-densitynon-calcified plaque.

Embodiment 7: The computer-implemented method of Embodiment 1, whereinthe distance from the one or more regions of low density non-calcifiedplaque to the lumen wall is determined by determining a shortestdistance between a boundary of the one or more regions of low densitynon-calcified plaque and a boundary of the lumen wall.

Embodiment 8: The computer-implemented method of Embodiment 1, whereinthe distance from the one or more regions of low density non-calcifiedplaque to the vessel wall is determined by determining a shortestdistance between a boundary of the one or more regions of low densitynon-calcified plaque and a boundary of the vessel wall.

Embodiment 9: The computer-implemented method of Embodiment 1, whereinthe one or more arteries comprises one or more coronary or carotidarteries.

Embodiment 10: The computer-implemented method of Embodiment 1, whereinthe one or more axes of the one or more regions of low densitynon-calcified plaque comprises one or more of a major axis on alongitudinal plane, minor axis on a longitudinal plane, major axis on alatitudinal plane, or minor axis on a latitudinal plane.

Embodiment 11: The computer-implemented method of Embodiment 10, whereinthe one or more axes are determined on a three-dimensional basis.

Embodiment 12: The computer-implemented method of Embodiment 10, whereinthe one or more axes are determined based on one or more two-dimensionalimages.

Embodiment 13: The computer-implemented method of Embodiment 12, whereinthe longitudinal plane is obtained by taking a two-dimensional sliceparallel to a longitudinal axis of a straightened multiplanar view ofthe one or more arteries.

Embodiment 14: The computer-implemented method of Embodiment 12, whereinthe longitudinal plane is obtained by taking a two-dimensional sliceresulting in a longest major axis of the longitudinal plane.

Embodiment 15: The computer-implemented method of Embodiment 12, whereinthe latitudinal plane is obtained by taking a two-dimensional sliceperpendicular to a longitudinal axis of a straightened multiplanar viewof the one or more arteries.

Embodiment 16: The computer-implemented method of Embodiment 12, whereinthe latitudinal plane is obtained by taking a two-dimensional sliceperpendicular to the major axis on the longitudinal plane.

Embodiment 17: The computer-implemented method of Embodiment 12, whereinthe latitudinal plane is obtained by taking a two-dimensional sliceresulting in a largest two-dimensional area of the low-densitynon-calcified plaque.

Embodiment 18: The computer-implemented method of Embodiment 1, whereinone or more of analyses of the one or more regions of low densitynon-calcified plaque is performed by the computer system.

Embodiment 19: The computer-implemented method of Embodiment 1, whereinthe degree of embeddedness of the one or more regions of low densitynon-calcified plaque is determined based at least in part by graphicallyoverlaying a protractor on the one or more regions of low densitynon-calcified plaque on the medical image.

Embodiment 20: The computer-implemented method of Embodiment 1, whereina higher degree of embeddedness of the one or more regions of lowdensity non-calcified plaque is indicative of an unstable plaque or highrisk of CAD.

Embodiment 21: The computer-implemented method of Embodiment 1, whereinthe shape of the one or more regions of low density non-calcified plaqueis determined as one or more of a crescent, round, lobular, or beanshape.

Embodiment 22: The computer-implemented method of Embodiment 21, whereindetermination of a round or bean shape of the one or more regions of lowdensity non-calcified plaque is indicative of an unstable plaque or highrisk of CAD.

Embodiment 23: The computer-implemented method of Embodiment 1, whereinthe shape of the one or more regions of low density non-calcified plaqueis determined based at least in part by a machine learning algorithm.

Embodiment 24: The computer-implemented method of Embodiment 1, whereinthe analysis of the one or more regions of low density non-calcifiedplaque further comprises determining one or more lengths of one or moreaxes of the one or more regions of low density non-calcified plaque.

Embodiment 25: The computer-implemented method of Embodiment 24, whereinthe shape of the one or more regions of low density non-calcified plaqueis determined based at least in part on the one or more determinedlengths of the one or more axes.

Embodiment 26: The computer-implemented method of Embodiment 25, whereinthe shape of the one or more regions of low density non-calcified plaqueis determined based at least in part on determining a standard deviationamong the one or more determined lengths of the one or more axes.

Embodiment 27: The computer-implemented method of Embodiment 24, whereinthe analysis of the one or more regions of low density non-calcifiedplaque further comprises: determining a volume of the one or moreregions of low density non-calcified plaque; determining a volume of theone or more regions of plaque; and determining a ratio of the volume ofthe one or more regions of low density non-calcified plaque to thevolume of the one or more regions of plaque.

Embodiment 28: The computer-implemented method of Embodiment 27, whereinthe volume of the one or more regions of low density non-calcifiedplaque is determined based on the one or more determined lengths of theone or more axes of the one or more regions of low density non-calcifiedplaque.

Embodiment 29: The computer-implemented method of Embodiment 27, whereina determination of the volume of the one or more regions of low densitynon-calcified plaque above a predetermined threshold is indicative ofunstable plaque or high risk of CAD.

Embodiment 30: The computer-implemented method of Embodiment 27, whereina determination of the volume of the one or more regions of plaque abovea predetermined threshold is indicative of unstable plaque or high riskof CAD.

Embodiment 31: The computer-implemented method of Embodiment 27, whereina determination of the ratio of the volume of the one or more regions oflow density non-calcified plaque to the volume of the one or moreregions of plaque above a predetermined threshold is indicative ofunstable plaque or high risk of CAD.

Embodiment 32: The computer-implemented method of Embodiment 1, whereinthe density comprises absolute density.

Embodiment 33: The computer-implemented method of Embodiment 1, whereinthe density comprises radiodensity.

Embodiment 34: The computer-implemented method of Embodiment 33, whereinthe one or more regions of plaque are identified as low densitynon-calcified plaque when a radiodensity value is between about −189 andabout 30 Hounsfield units.

Embodiment 35: The computer-implemented method of Embodiment 33, whereinthe one or more regions of plaque are identified as non-calcified plaquewhen a radiodensity value is between about 31 and about 350 Hounsfieldunits.

Embodiment 36: The computer-implemented method of Embodiment 33, whereinthe one or more regions of plaque are identified as calcified plaquewhen a radiodensity value is between about 351 and 2500 Hounsfieldunits.

Embodiment 37: The computer-implemented method of Embodiment 1, whereinthe medical image comprises a Computed Tomography (CT) image.

Embodiment 38: The computer-implemented method of Embodiment 1, whereinthe medical image is obtained using an imaging technique comprising oneor more of CT, x-ray, ultrasound, echocardiography, MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 39: The computer-implemented method of Embodiment 1, furthercomprising generating, by the computer system, an assessment of risk ofCAD of the subject or risk of the one or more regions of plaque based atleast in part on the analysis of the one or more regions of low densitynon-calcified plaque.

Embodiment 40: The computer-implemented method of Embodiment 39, furthercomprising generating, by the computer system, a recommended treatmentfor the subject based at least in part on the analysis of the one ormore regions of low density non-calcified plaque.

Embodiment 41: A system for facilitating determination of risk ofcoronary artery disease (CAD) based at least in part on one or moremeasurements derived from non-invasive medical image analysis, thesystem comprising: one or more computer readable storage devicesconfigured to store a plurality of computer executable instructions; andone or more hardware computer processors in communication with the oneor more computer readable storage devices and configured to execute theplurality of computer executable instructions in order to cause thesystem to: access a medical image of a subject, wherein the medicalimage of the subject is obtained non-invasively; analyze the medicalimage of the subject to identify one or more arteries; identify one ormore regions of plaque within the one or more coronary arteries; analyzethe identified one or more regions of plaque to identify one or moreregions of low density non-calcified plaque, non-calcified plaque, orcalcified plaque based at least in part on density, facilitateanalyzing, in response to identifying one or more regions of low densitynon-calcified plaque, the one or more regions of low densitynon-calcified plaque, wherein the analysis of the one or more regions oflow density non-calcified plaque comprises: determining a distance fromthe one or more regions of low density non-calcified plaque to one ormore of a lumen wall or vessel wall; determining a degree ofembeddedness of the one or more regions of low density non-calcifiedplaque by one or more of non-calcified plaque or calcified plaque; anddetermining a shape of the one or more regions of low densitynon-calcified plaque; and

generate a display of the analysis of the one or more regions of lowdensity non-calcified plaque to facilitate determination of one or moreof a risk of CAD of the subject based at least in part on the analysisof the one or more regions of low density non-calcified plaque.

Embodiment 42: The system of Embodiment 41, wherein a determination ofthe distance from the one or more regions of low density non-calcifiedplaque to the lumen wall below a predetermined threshold is indicativeof an unstable plaque or high risk of CAD.

Embodiment 43: The system of Embodiment 41, wherein the distance fromthe one or more regions of low density non-calcified plaque to one ormore of the lumen wall or vessel wall is determined on athree-dimensional basis.

Embodiment 44: The system of Embodiment 41, wherein the distance fromthe one or more regions of low density non-calcified plaque to one ormore of the lumen wall or vessel wall is determined based on atwo-dimensional image.

Embodiment 45: The system of Embodiment 44, wherein the two-dimensionalimage is obtained by taking a two-dimensional slice perpendicular to alongitudinal axis of a straightened multiplanar view of the one or morearteries.

Embodiment 46: The system of Embodiment 44, wherein the two-dimensionalimage is obtained by taking a two-dimensional slice resulting in alargest two-dimensional area of the low-density non-calcified plaque.

Embodiment 47: The system of Embodiment 41, wherein the distance fromthe one or more regions of low density non-calcified plaque to the lumenwall is determined by determining a shortest distance between a boundaryof the one or more regions of low density non-calcified plaque and aboundary of the lumen wall.

Embodiment 48: The system of Embodiment 41, wherein the distance fromthe one or more regions of low density non-calcified plaque to thevessel wall is determined by determining a shortest distance between aboundary of the one or more regions of low density non-calcified plaqueand a boundary of the vessel wall.

Embodiment 49: The system of Embodiment 41, wherein the one or morearteries comprises one or more coronary or carotid arteries.

Embodiment 50: The system of Embodiment 41, wherein the one or more axesof the one or more regions of low density non-calcified plaque comprisesone or more of a major axis on a longitudinal plane, minor axis on alongitudinal plane, major axis on a latitudinal plane, or minor axis ona latitudinal plane.

Embodiment: 51 The system of Embodiment 50, wherein the one or more axesare determined on a three-dimensional basis.

Embodiment 52: The system of Embodiment 50, wherein the one or more axesare determined based on one or more two-dimensional images.

Embodiment 53: The system of Embodiment 52, wherein the longitudinalplane is obtained by taking a two-dimensional slice parallel to alongitudinal axis of a straightened multiplanar view of the one or morearteries.

Embodiment 54: The system of Embodiment 52, wherein the longitudinalplane is obtained by taking a two-dimensional slice resulting in alongest major axis of the longitudinal plane.

Embodiment 55: The system of Embodiment 52, wherein the latitudinalplane is obtained by taking a two-dimensional slice perpendicular to alongitudinal axis of a straightened multiplanar view of the one or morearteries.

Embodiment 56: The system of Embodiment 52, wherein the latitudinalplane is obtained by taking a two-dimensional slice perpendicular to themajor axis on the longitudinal plane.

Embodiment 57: The system of Embodiment 52, wherein the latitudinalplane is obtained by taking a two-dimensional slice resulting in alargest two-dimensional area of the low-density non-calcified plaque.

Embodiment 58: The system of Embodiment 41, wherein one or more ofanalyses of the one or more regions of low density non-calcified plaqueis performed by the computer system.

Embodiment 59: The system of Embodiment 41, wherein the degree ofembeddedness of the one or more regions of low density non-calcifiedplaque is determined based at least in part by graphically overlaying aprotractor on the one or more regions of low density non-calcifiedplaque on the medical image.

Embodiment 60: The system of Embodiment 41, wherein a higher degree ofembeddedness of the one or more regions of low density non-calcifiedplaque is indicative of an unstable plaque or high risk of CAD.

Embodiment 61: The system of Embodiment 41, wherein the shape of the oneor more regions of low density non-calcified plaque is determined as oneor more of a crescent, round, lobular, or bean shape.

Embodiment 62: The system of Embodiment 61, wherein determination of around or bean shape of the one or more regions of low densitynon-calcified plaque is indicative of an unstable plaque or high risk ofCAD.

Embodiment 63: The system of Embodiment 41, wherein the shape of the oneor more regions of low density non-calcified plaque is determined basedat least in part by a machine learning algorithm.

Embodiment 64: The system of Embodiment 41, wherein the analysis of theone or more regions of low density non-calcified plaque furthercomprises determining one or more lengths of one or more axes of the oneor more regions of low density non-calcified plaque.

Embodiment 65: The system of Embodiment 64, wherein the shape of the oneor more regions of low density non-calcified plaque is determined basedat least in part on the one or more determined lengths of the one ormore axes.

Embodiment 66: The system of Embodiment 65, wherein the shape of the oneor more regions of low density non-calcified plaque is determined basedat least in part on determining a standard deviation among the one ormore determined lengths of the one or more axes.

Embodiment 67: The system of Embodiment 64, wherein the analysis of theone or more regions of low density non-calcified plaque furthercomprises: determining a volume of the one or more regions of lowdensity non-calcified plaque; determining a volume of the one or moreregions of plaque; and determining a ratio of the volume of the one ormore regions of low density non-calcified plaque to the volume of theone or more regions of plaque.

Embodiment 68: The system of Embodiment 67, wherein the volume of theone or more regions of low density non-calcified plaque is determinedbased on the one or more determined lengths of the one or more axes ofthe one or more regions of low density non-calcified plaque.

Embodiment 69: The system of Embodiment 67, wherein a determination ofthe volume of the one or more regions of low density non-calcifiedplaque above a predetermined threshold is indicative of unstable plaqueor high risk of CAD.

Embodiment 70: The system of Embodiment 67, wherein a determination ofthe volume of the one or more regions of plaque above a predeterminedthreshold is indicative of unstable plaque or high risk of CAD.

Embodiment 71: The system of Embodiment 67, wherein a determination ofthe ratio of the volume of the one or more regions of low densitynon-calcified plaque to the volume of the one or more regions of plaqueabove a predetermined threshold is indicative of unstable plaque or highrisk of CAD.

Embodiment 72: The system of Embodiment 41, wherein the densitycomprises absolute density.

Embodiment 73: The system of Embodiment 41, wherein the densitycomprises radiodensity.

Embodiment 74: The system of Embodiment 73, wherein the one or moreregions of plaque are identified as low density non-calcified plaquewhen a radiodensity value is between about −189 and about 30 Hounsfieldunits.

Embodiment 75: The system of Embodiment 73, wherein the one or moreregions of plaque are identified as non-calcified plaque when aradiodensity value is between about 31 and about 350 Hounsfield units.

Embodiment 76: The system of Embodiment 73, wherein the one or moreregions of plaque are identified as calcified plaque when a radiodensityvalue is between about 351 and 2500 Hounsfield units.

Embodiment 77: The system of Embodiment 41, wherein the medical imagecomprises a Computed Tomography (CT) image.

Embodiment 78: The system of Embodiment 41, wherein the medical image isobtained using an imaging technique comprising one or more of CT, x-ray,ultrasound, echocardiography, MR imaging, optical coherence tomography(OCT), nuclear medicine imaging, positron-emission tomography (PET),single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 79: The system of Embodiment 41, wherein the system isfurther caused to generate an assessment of risk of CAD of the subjector risk of the one or more regions of plaque based at least in part onthe analysis of the one or more regions of low density non-calcifiedplaque.

Embodiment 80: The system of Embodiment 79, wherein the system isfurther caused to generate a recommended treatment for the subject basedat least in part on the analysis of the one or more regions of lowdensity non-calcified plaque.

Unfolding of a Vessel

As discussed herein, disclosed herein are systems, methods, and devicesfor cardiovascular risk and/or disease state assessment usingimage-based analyses. In particular, in some embodiments, the systems,devices, and methods are related to cardiovascular risk and/or diseasestate assessment using image-based analysis of vessel surface and/orcoordinates of related features. In some embodiments, assessment ofcardiovascular risk and/or disease state generated using the systems,methods, and devices herein can be utilized to diagnose and/or generatea proposed treatment for a patient. As such, in some embodiments, thesystems, devices, and methods described herein are able to providephysicians and/or patients specific quantified and/or measured datarelating to a patient's plaque that do not exist today. For example, insome embodiments, the system can provide a specific numerical value forthe volume of stable and/or unstable plaque, the ratio thereof againstthe total vessel volume, percentage of stenosis, and/or the like, usingfor example radiodensity values of pixels and/or regions within amedical image. In some embodiments, such detailed level of quantifiedplaque parameters from image processing and downstream analyticalresults can provide more accurate and useful tools for assessing thehealth and/or risk of patients in completely novel ways.

In some embodiments, the systems, devices, and methods described hereincan be configured to unfold a generally cylindrical blood vessel, suchas a coronary artery, carotid artery, and/or any other blood vessel forexample, and flatten out the vessel for further analysis. In otherwords, in some embodiments, the system is configured to computationallyflatten or unfold a generally cylindrical blood vessel, for exampleusing one or more graphical or image-analysis techniques. By doing so,in some embodiments, the system can more easily, more accurately, and/ormore quickly evaluate, analyze, and/or diagnose a state or profession ofcardiovascular disease, such as plaque or atherosclerosis, and/or usethe same for disease tracking and/or treatment purposes. In particular,by flattening out unfolding a generally cylindrical blood vessel, insome embodiments, the system can be configured to expose one or moreregions of plaque internal to the vessel. In some embodiments, theexposed part or portion of the plaque can be used to determine the riskof myocardial infarction (MI), ischemia, rapid plaque progression,medication plaque non-responsiveness, need for a stent, need for bypassgrafting, and/or the like.

FIG. 10A is a schematic illustrating an example embodiment(s) ofsystems, methods, and devices for cardiovascular risk and/or diseasestate assessment using image-based analysis of vessel surface and/orcoordinates. As illustrated in the example of FIG. 10A, the system canbe configured to unfold or flatten an otherwise generally cylindricaltube of the vessel 1000 to be viewed as a rectangle 1008. Morespecifically, in some embodiments, the system is configured to identifya generally cylindrical blood vessel 1000, which can include one or moreregions of plaque 1002, 1004. In some instances, plaque can bepositioned internal to the lumen 1003 (e.g., plaque 1002). In someinstances, plaque can be positioned external to the lumen 1003 (e.g.,plaque 1004), either inside the vessel wall 1005, or positioned adjacentto the exterior vessel wall 1009 and external to the vessel 1000. Insome instances when the plaque is in the vessel wall, the plaque causesthe vessel wall to protrude into the lumen. For example, protrude intoat least a portion of the space where the lumen would be if not for theplaque causing the vessel wall to extend into that space. In someembodiments, the system can be configured to unfold or flatten thegenerally cylindrical tube of a vessel 1000 along a hypothetical cutline 1006 that is generally parallel to the longitudinal axis of thevessel 1000, e.g., a hypothetical cut line 1006 that is in a plane thatalso contains the longitudinal vessel 1000. In some embodiments, forexample for ease of processing, prior to the unfolding or flattening ofa vessel, the system is configured to generate a straightenedrepresentation of an otherwise curvilinear vessel, and process thestraightened representation to unfold or flatten the vessel. In anexample, the width w of the representation can be the circumference ofthe vessel, and the length l can be the length of the representation canbe the portion of the vessel being evaluated.

In some embodiments, by unfolding the vessel, the system can beconfigured to view and/or display the internal of a vessel as flat orrectangular view (representation)1008 on a user interface of a displayscreen, for example, a graphical user display (GUI). In someembodiments, the system can be configured to display the flat orrectangular representation in a three-dimensional (3D) view. In someembodiments, the system can be configured to rotate the 3D view in a360° manner to allow a medical practitioner to better view and evaluatethe plaque. In some embodiments, by doing so, some of the plaque 1002can be exposed, while some of the plaque 1004 may not be exposed. Forexample, in some embodiments, regions of plaque facing the internallumen of the vessel 1002 can be exposed, while regions of plaque 1004facing outwards of the lumen 1003 may not be exposed. In someembodiments, by flattening or unfolding a cylindrical vessel 1000, thesystem can be configured to utilize a cartesian or other coordinatesystem to characterize the surface area of plaque and/or the locationand/or position of different components within a region of plaque, whichcan be used to assess the risk and/or state of cardiovascular diseasefor a subject more accurately, faster, and/or using less processingpower. In some embodiments, the system can be configured to display theflat or rectangular representation in a three-dimensional (3D) view. Insome embodiments, the system can be configured to rotate the 3D view ina 360° manner to allow a medical practitioner to better view andevaluate the plaque that is exposed, or unexposed, by flattening thevessel.

In some embodiments, after unfolding or flattening a vessel, the systemis configured to analyze different components of a region of plaque,such as for example low-density non-calcified plaque, non-calcifiedplaque, calcified plaque, and/or the like. In some embodiments, thelocation and/or position of such individual plaque components and/or thewhole region of plaque itself can be analyzed by the system using one ormore coordinate systems after flattening and/or unfolding of the vessel.

In some embodiments, by flattening or unfolding the cylindrical tube ofthe vessel as a rectangle, the system can be configured to visualizeand/or analyze the exposed surface area in one or more ways. Forexample, in some embodiments, once unfolded, the system can beconfigured to analyze, identify, color, annotate, and/or otherwisegraphically alter one or more of the following features: surface area ofplaque or exposed plaque, surface area of plaque or exposed plaquecompared to total internal surface area of vessel segment, thickness ofvessel and/or plaque and/or ratio thereof, composition of plaque (e.g.,low-density non-calcified plaque, calcified plaque, and/or non-calcifiedplaque), stenosis of vessel lumen, remodeling index, heterogeneity ofplaque (e.g., as a function or combination of low-density non-calcifiedplaque, calcified plaque, and/or non-calcified plaque), surfaceirregularity, surface asymmetry, surface ulceration, depth to one ormore plaque components (e.g., low-density non-calcified plaque,calcified plaque, and/or non-calcified plaque), and/or the like.

In some embodiments, the system can be configured to apply a coordinatesystem to the flattened or unfolded vessel to analyze plaque and/orindividual plaque components. For example, in some embodiments, thesystem can be configured to utilize a cartesian coordinate system, polarcoordinate system, cylindrical coordinate system, spherical coordinatesystem, homogeneous coordinate system, curvilinear coordinate system,log-polar coordinate system, Plucker coordinate system, generalizedcoordinate system, canonical coordinate system, barycentric coordinatesystem, and/or trilinear coordinate system.

In some embodiments, the system can be configured to utilizecoordinates, such as cartesian coordinates, of plaque and/or individualplaque components to predict and/or assess risk and/or state ofcardiovascular disease. In some embodiments, risk and/or disease stateassessment can be dependent on the position of the plaque and/or plaquecomponent relative to one or more of: one or more regions of fat, one ormore other regions of plaque, vessel lumen, myocardium, myocardial sidethe of vessel, pericardium, pericardial side of the vessel, epicardialfat, epicardial fat side of the vessel, branch point, bifurcation,trifurcation, or distance from vessel ostium. In some embodiments, thesystem can be configured to utilize coordinates of plaque and/or plaquecomponents to determine a Euclidian distance from one or more of theforegoing.

Location of Plaque Relative to Myocardium v. Epicardium

FIG. 10B is a schematic illustrating an example of one or more regionsof plaque that are myocardial v. epicardial fat-adjacent. As illustratedin FIG. 10B, in some instances, plaque can be adjacent, proximal to,near, and/or closer to the myocardium v. epicardium, which can beindicative of risk of cardiovascular disease. For instance, in theexample illustrated in FIG. 10B, a first region of plaque 1024 within avessel 1000 can be adjacent, proximal to, near, and/or closer to theepicardial fat side, while a second region of plaque 1022 within thesame vessel 1000 can be adjacent, proximal to, near, and/or closer tothe myocardium side. In the illustrated example, the first region ofplaque 1024 closer to the epicardial side be associated with a higherrisk of disease compared to the second region of plaque 1022 closer tothe myocardial side. As such, in some embodiments, the system can beconfigured to analyze the location of one or more regions of plaqueand/or one or more components thereof, for example by mapping to acoordinate system after computationally unfolding a vessel, to determinethe location and/or relative location of the region of plaque or acomponent thereof with respect to the epicardium and/or myocardium. Insome embodiments, the analyzed location of the one or more regions ofplaque and/or one or more components thereof can be used by the systemto determine risk of cardiovascular disease.

Cardiovascular Risk and/or Disease State Assessment Using Image-BasedAnalysis of Vessel Surface and/or Coordinates of Features

FIG. 10C is a flowchart illustrating an example embodiment(s) ofsystems, methods, and devices for cardiovascular risk and/or diseasestate assessment using image-based analysis of vessel surface and/orcoordinates.

As illustrated in FIG. 10C, in some embodiments, the system can beconfigured to access a medical image at block 1032. In some embodiments,the medical image can include one or more arteries, such as coronary,carotid, and/or other arteries of a subject. In some embodiments, themedical image can be stored in a medical image database 1056. In someembodiments, the medical image database 1056 can be locally accessibleby the system and/or can be located remotely and accessible through anetwork connection. The medical image can comprise an image obtain usingone or more modalities such as for example, CT, Dual-Energy ComputedTomography (DECT), Spectral CT, photon-counting CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), Magnetic Resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).In some embodiments, the medical image comprises one or more of acontrast-enhanced CT image, non-contrast CT image, MR image, and/or animage obtained using any of the modalities described above.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 1036, thesystem can be configured to identify one or more vessels or arteries.The one or more arteries can include coronary arteries, carotidarteries, aorta, renal artery, lower extremity artery, upper extremityartery, and/or cerebral artery, amongst others. In some embodiments, thesystem can be configured to utilize one or more AI and/or ML algorithmsto automatically and/or dynamically identify one or more arteries orcoronary arteries using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich arteries or coronary arteries have been identified, therebyallowing the AI and/or ML algorithm automatically identify arteries orcoronary arteries directly from a medical image. In some embodiments,the arteries or coronary arteries are identified by size and/orlocation.

In some embodiments, at block 1038, the system can be configured toidentify a hypothetical and/or computational cut line for one or morevessels. In particular, in some embodiments the hypothetical cut linecan be substantially parallel, perpendicular, and/or at another anglewith respect to the longitudinal axis of a vessel. In some embodiments,prior to identifying the hypothetical cut line, the system can beconfigured to computationally straighten out a vessel into asubstantially straight cylindrical vessel form. In some embodiments, thesystem can be configured to utilize one or more AI and/or ML algorithmsto automatically and/or dynamically straighten a vessel and/or identifya cut line.

In some embodiments, at block 1040, the system can be configured tocomputationally unfold a vessel, for example along the hypothetical cutline. In some embodiments, at block 1042, the system can be configuredto identify one or more regions of plaque on the unfolded vessel. Forexample, the one or more regions of plaque can be exposed on theunfolded vessel, which can mean that the plaque was internal to thevessel prior to unfolding. Also, in some instances, the one or moreregions of plaque can be unexposed on the unfolded vessel, which canmean that the plaque was external to the vessel prior to unfolding. Insome embodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically identify one ormore plaques on an unfolded vessel.

In some embodiments, at block 1044, the system can be configured toanalyze one or more plaque parameters and/or one or more vessel orvascular parameters. In some embodiments, the one or more vesselparameters and/or plaque parameters can comprise quantified parametersderived from the medical image. For example, in some embodiments, thesystem can be configured to utilize an AI and/or ML algorithm or otheralgorithm to determine one or more vessel parameters and/or plaqueparameters. As another example, in some embodiments, the system can beconfigured to determine one or more vessel parameters, such asclassification of arterial remodeling due to plaque, which can furtherinclude positive arterial remodeling, negative arterial remodeling,and/or intermediate arterial remodeling. In some embodiments, theclassification of arterial remodeling is determined based on a ratio ofthe largest vessel diameter at a region of plaque to a normal referencevessel diameter of the same region which can be retrieved from a normaldatabase. In some embodiments, the system can be configured to classifyarterial remodeling as positive when the ratio of the largest vesseldiameter at a region of plaque to a normal reference vessel diameter ofthe same region is more than 1.1. In some embodiments, the system can beconfigured to classify arterial remodeling as negative when the ratio ofthe largest vessel diameter at a region of plaque to a normal referencevessel diameter is less than 0.95. In some embodiments, the system canbe configured to classify arterial remodeling as intermediate when theratio of the largest vessel diameter at a region of plaque to a normalreference vessel diameter is between 0.95 and 1.1.

Further, as part of block 1044, in some embodiments, the system can beconfigured to determine a geometry and/or volume of one or more regionsof plaque and/or one or more vessels or arteries. For example, thesystem can be configured to determine if the geometry of a particularregion of plaque is round or oblong or other shape. In some embodiments,the geometry of a region of plaque can be a factor in assessing thestability of the plaque. As another example, in some embodiments, thesystem can be configured to determine the curvature, diameter, length,volume, and/or any other parameters of a vessel or artery from themedical image.

In some embodiments, as part of block 1044, the system can be configuredto determine a volume and/or surface area of a region of plaque and/or aratio or other function of volume to surface area of a region of plaque,such as for example a diameter, radius, and/or thickness of a region ofplaque. In some embodiments, a plaque having a low ratio of volume tosurface area can indicate that the plaque is stable. As such, in someembodiments, the system can be configured to determine that a ratio ofvolume to surface area of a region of plaque below a predeterminedthreshold is indicative of stable plaque.

In some embodiments, as part of block 1044, the system can be configuredto determine a heterogeneity index of a region of plaque. For instance,in some embodiments, a plaque having a low heterogeneity or highhomogeneity can indicate that the plaque is stable. As such, in someembodiments, the system can be configured to determine that aheterogeneity of a region of plaque below a predetermined threshold isindicative of stable plaque. In some embodiments, heterogeneity orhomogeneity of a region of plaque can be determined based on theheterogeneity or homogeneity of radiodensity values within the region ofplaque. As such, in some embodiments, the system can be configured todetermine a heterogeneity index of plaque by generating spatial mapping,such as a three-dimensional histogram, of radiodensity values within oracross a geometric shape or region of plaque. In some embodiments, if agradient or change in radiodensity values across the spatial mapping isabove a certain threshold, the system can be configured to assign a highheterogeneity index. Conversely, in some embodiments, if a gradient orchange in radiodensity values across the spatial mapping is below acertain threshold, the system can be configured to assign a lowheterogeneity index.

In some embodiments, as part of block 1044, the system can be configuredto determine a radiodensity of plaque and/or a composition thereof. Forexample, a high radiodensity value can indicate that a plaque is highlycalcified or stable, whereas a low radiodensity value can indicate thata plaque is less calcified or unstable. As such, in some embodiments,the system can be configured to determine that a radiodensity of aregion of plaque above a predetermined threshold is indicative of stablestabilized plaque. In addition, different areas within a region ofplaque can be calcified at different levels and thereby show differentradiodensity values. As such, in some embodiments, the system can beconfigured to determine the radiodensity values of a region of plaqueand/or a composition or percentage or change of radiodensity valueswithin a region of plaque. For instance, in some embodiments, the systemcan be configured to determine how much or what percentage of plaquewithin a region of plaque shows a radiodensity value within a low range,medium range, high range, and/or any other classification.

Similarly, in some embodiments, as part of block 1044, the system can beconfigured to determine a ratio of radiodensity value of plaque to avolume of plaque. For instance, it can be important to assess whether alarge or small region of plaque is showing a high or low radiodensityvalue. As such, in some embodiments, the system can be configured todetermine a percentage composition of plaque comprising differentradiodensity values as a function or ratio of volume of plaque.

In some embodiments, as part of block 1044, the system can be configuredto determine the diffusivity and/or assign a diffusivity index to aregion of plaque. For example, in some embodiments, the diffusivity of aplaque can depend on the radiodensity value of plaque, in which a highradiodensity value can indicate low diffusivity or stability of theplaque.

In some embodiments, at block 1046, the system can be configured toanalyze the location and/or position of one or more regions of plaqueand/or one or more components thereof, such as for example low-densitynon-calcified plaque, calcified plaque, and/or non-calcified plaque. Forexample, in some embodiments, the system can be configured to analyzethe location and/or position of one or more regions of plaque and/orcomponents thereof by mapping to a coordinate system and/or bydetermining its Euclidian distance. For example, in some embodiments,the system can be configured to utilize a cartesian coordinate system,polar coordinate system, cylindrical coordinate system, sphericalcoordinate system, homogeneous coordinate system, curvilinear coordinatesystem, log-polar coordinate system, Plucker coordinate system,generalized coordinate system, canonical coordinate system, barycentriccoordinate system, and/or trilinear coordinate system.

In some embodiments, at block 1048, the system can be configured todetermine and/or generate a risk and/or disease state assessment of oneor more regions of plaque with respect to cardiovascular disease. Forexample, in some embodiments, the system can be configured to determineand/or generate a risk and/or disease state assessment of one or moreregions of plaque based on one or more vessel and/or plaque parametersand/or position or location of one or more regions of plaque and/orcomponents thereof as discussed herein. In some embodiments, the systemcan be configured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically determine and/or generate a risk and/ordisease state assessment of one or more regions of plaque with respectto cardiovascular disease. In some embodiments, the system can beconfigured to determine and/or generate a cardiovascular disease riskand/or state assessment of one or more regions of plaque by comparisonand/or based on analysis of one or more previous analyses, which can bestored for example on a plaque risk database 1054. In some embodiments,the plaque risk database 1054 can be locally accessible by the systemand/or can be located remotely and accessible through a networkconnection.

In some embodiments, the system can be configured to graphicallyvisualize one or more regions of plaque, components thereof, and/orvessel features at block 1050. For example, the graphical representationcan depend on the generated cardiovascular risk and/or disease stateassessment. As an illustrative example, the system can be configured toanalyze, identify, color, annotate, and/or otherwise graphically alterone or more of the following features: surface area of plaque or exposedplaque, surface area of plaque or exposed plaque compared to totalinternal surface area of vessel segment, thickness of vessel and/orplaque and/or ratio thereof, composition of plaque (e.g., low-densitynon-calcified plaque, calcified plaque, and/or non-calcified plaque),stenosis of vessel lumen, remodeling index, heterogeneity of plaque(e.g., as a function or combination of low-density non-calcified plaque,calcified plaque, and/or non-calcified plaque), surface irregularity,surface asymmetry, surface ulceration, depth to one or more plaquecomponents (e.g., low-density non-calcified plaque, calcified plaque,and/or non-calcified plaque), and/or the like. In generating a graphicalrepresentation or visualization, in some embodiments, the system can beconfigured to assign to one or more components or features one or moreof color, shading, translucency, three-dimensional annotation, orpartial translucency.

In some embodiments, at block 1052, the system can be configured todetermine an aggregate cardiovascular risk and/or disease state for asubject based at least in part on the determined risk and/or diseasestate of one or more regions of plaque. For example, in someembodiments, the system can be configured to generate a weighted averageor weighted combination or measure of all or some of the generatedcardiovascular disease risks of the one or more regions of plaque. Insome embodiments, the generated risk score can be based at least in parton an atherosclerotic cardiovascular disease (ASCVD) risk score. Forexample, an ASCVD risk score can be determined based on patientinformation, and then generated risk score can be based in part on theASCVD risk score. There can be various ways to determine an ASCVD riskscore, including using/weighting various patient information todetermine an overall ASCVD risk score. In an example, patientinformation used to determine the ASCVD risk score can include, but isnot limited to, age, gender, race, systolic blood pressure, diastolicblood pressure, total cholesterol, high-density lipoprotein (HDL)cholesterol, low-density lipoprotein (LDL) cholesterol, history ofdiabetes, whether a current or past smoker, hypertension, and/or currentmedication (e.g., on a statin or aspirin therapy). In an example, theAmerican College of Cardiology indicates use of some or all of thisinformation to determine an ASCVD risk score. In some embodiments, aASCVD risk score can considered Low-risk if <5%, Borderline risk if 5%to 7.4%, Intermediate risk if 7.5% to 19.9%, and High risk if above 20%.In some embodiments, a current patient's aggregate cardiovascular riskand/or disease state for a subject can be based in part on an ASCVD riskscore that is compared to other patients' ASCVD risk scores and outcomes(e.g., that are stored in a database).

In some embodiments, the system can be configured to determine and/orgenerate a proposed treatment for the subject based at least in part onthe determined risk and/or disease state of one or more regions ofplaque. In some embodiments, the system can be configured to determineand/or generate a proposed treatment for the subject based at least inpart on the aggregate cardiovascular risk and/or disease state for thesubject.

In some embodiments, the system can be configured to determine and/orgenerate an aggregate cardiovascular disease risk and/or stateassessment and/or proposed treatment for the subject by comparisonand/or based on analysis of one or more previously analyses, which canbe stored for example on a plaque risk database 1054. In someembodiments, the plaque risk database 1054 can be locally accessible bythe system and/or can be located remotely and accessible through anetwork connection.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to flowchart blocks 1032-1052, forexample for one or more other regions of plaque and/or other subjectsand/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease trackingand/or personalized treatment for a subject.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 10D. The example computer system 1070 is incommunication with one or more computing systems 1088 and/or one or moredata sources 1090 via one or more networks 1086. While FIG. 10Dillustrates an embodiment of a computing system 1070, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1070 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1070 can comprise a Plaque Analysis and/or RiskAssessment Module 1082 that carries out the functions, methods, acts,and/or processes described herein. The Plaque Analysis and/or RiskAssessment Module 1082 executed on the computer system 1070 by a centralprocessing unit 1078 discussed further below. Other features of thecomputer system 1070 can be similar to corresponding features of thecomputer system of FIG. 9G, described above.

Certain Examples of Embodiments Related to Unfolding of a Vessel

The following are non-limiting examples of certain embodiments ofsystems and methods for analyzing a vessel by unfolding the vessel andcharacterization of plaque parameters and vessel parameters, and/orother related features. Other embodiments may include one or more otherfeatures, or different features, that are discussed herein.

Embodiment 1: A computer-implemented method of assessing a state ofcardiovascular disease for a subject based on multi-dimensionalinformation derived from non-invasive medical image analysis, the methodcomprising: accessing, by a computer system, a medical image of asubject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries, wherein the one or morearteries comprise one or more regions of plaque; identifying, by thecomputer system, a hypothetical cut line along the one or more arteries,wherein the hypothetical cut line is substantially parallel to alongitudinal axis of the one or more arteries; computationallyunfolding, by the computer system, the one or more arteries along thehypothetical cut line; identifying, by the computer system, one or moreregions of plaque on the computationally unfolded one or more arteries;identifying, by the computer system, one or more regions of exposedplaque among the one or more regions of plaque on the computationallyunfolded one or more arteries; analyzing, by the computer system, theone or more regions of exposed plaque to determine one or more plaqueparameters and one or more vessel parameters, wherein the one or moreplaque parameters comprises one or more of surface area of plaque,thickness of plaque, composition of plaque, heterogeneity of plaque, ordepth to a plaque component, and wherein the one or more vesselparameters comprises one or more of surface area of vessel wall,thickness of vessel wall, stenosis of vessel lumen, remodeling index,surface irregularity, surface asymmetry, or surface ulceration; mapping,by the computer system, the one or more regions of exposed plaque andthe computationally unfolded one or more arteries to a coordinatesystem; analyzing, by the computer system, a position of the one or moreregions of exposed plaque based at least in part on the mapping of theone or more regions of exposed plaque and the computationally unfoldedone or more arteries to a coordinate system; generating, by the computersystem, an assessment of a cardiovascular disease state of the one ormore regions of exposed plaque based at least in part on the determinedone or more plaque parameters, one or more vessel parameters, andanalyzed position of the one or more regions of exposed plaque, whereinthe computer system comprises a computer processor and an electronicstorage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 3: The computer-implemented method of Embodiment 1, whereinthe hypothetical cut line is identified on a straightened view of theone or more arteries.

Embodiment 4: The computer-implemented method of Embodiment 1, whereinthe one or more regions of plaque comprise the one or more regions ofexposed plaque and one or more regions of unexposed plaque.

Embodiment 5: The computer-implemented method of Embodiment 1, whereinthe coordinate system comprises one or more of a cartesian coordinatesystem, polar coordinate system, cylindrical coordinate system,spherical coordinate system, homogeneous coordinate system, curvilinearcoordinate system, log-polar coordinate system, Plucker coordinatesystem, generalized coordinate system, canonical coordinate system,barycentric coordinate system, or trilinear coordinate system.

Embodiment 6: The computer-implemented method of Embodiment 1, furthercomprising: determining, by the computer system, one or more plaque tovessel parameters, the one or more plaque to vessel parameterscomprising one or more of a ratio of surface area of plaque to surfacearea of vessel or a ratio of thickness of plaque to thickness of vessel;and generating, by the computer system, the cardiovascular riskassessment of the subject further based at least in part on thedetermined one or more plaque to vessel parameters.

Embodiment 7: The computer-implemented method of Embodiment 1, whereinthe heterogeneity of plaque is determined by identifying one or morecomponents of plaque within a region of plaque, wherein the one or morecomponents of plaque comprise one or more of low density non-calcifiedplaque, calcified plaque, or non-calcified plaque.

Embodiment 8: The computer-implemented method of Embodiment 1, whereinthe plaque component comprises one or more of low density non-calcifiedplaque, calcified plaque, or non-calcified plaque.

Embodiment 9: The computer-implemented method of Embodiment 1, whereinthe position of the one or more regions of exposed plaque is determinedas a Euclidian distance from one or more locations of interest.

Embodiment 10: The computer-implemented method of Embodiment 1, whereinthe position of the one or more regions of exposed plaque is determinedin relation to one or more of one or more regions of fat, one or moreother regions of plaque, vessel lumen, myocardium, myocardial side theof vessel, pericardium, pericardial side of the vessel, epicardial fat,epicardial fat side of the vessel, branch point, bifurcation,trifurcation, or distance from vessel ostium.

Embodiment 11: The computer-implemented method of Embodiment 10, whereina higher risk is assessed for one or more regions of exposed plaque thatis adjacent to the epicardial side of the vessel compared to one or moreregions of exposed plaque that is adjacent to the myocardial side of thevessel.

Embodiment 12: The computer-implemented method of Embodiment 1, furthercomprising generating a graphical representation of the one or moreregions of exposed plaque based on the generated cardiovascular diseaserisk assessment of the one or more regions of exposed plaque.

Embodiment 13: The computer-implemented method of Embodiment 1, whereingenerating the graphical representation comprises assigning one or moreof color, shading, translucency, three-dimensional annotation, orpartial translucency.

Embodiment 14: The computer-implemented method of Embodiment 1, furthercomprising generating a graphical representation of one or morecomponents within the one or more regions of exposed plaque based on thegenerated cardiovascular disease risk assessment of the one or moreregions of exposed plaque.

Embodiment 15: The computer-implemented method of Embodiment 1, whereinthe cardiovascular disease risk assessment of the one or more regions ofexposed plaque is generated by utilizing an artificial intelligence ormachine learning algorithm based on prior subject image analysis data.

Embodiment 16: The computer-implemented method of Embodiment 1, furthercomprising generating, by the computer system, cardiovascular diseaserisk assessment of all regions of exposed plaque within a vesselsegment.

Embodiment 17: The computer-implemented method of Embodiment 16, furthercomprising generating a cardiovascular disease risk assessment of thesubject by combining the generated cardiovascular disease riskassessment of all regions of exposed plaque within the vessel segment.

Embodiment 18: The computer-implemented method of Embodiment 17, whereinthe cardiovascular disease risk assessment of the subject is generatedbased at least in part on comparison to a database of previouslygenerated cardiovascular disease risk assessments of other subjects.

Embodiment 19: The computer-implemented method of Embodiment 16, furthercomprising generating a proposed treatment for the subject by combiningthe generated cardiovascular disease risk assessment of all regions ofexposed plaque within the vessel segment.

Embodiment 20: The computer-implemented method of Embodiment 19, whereinthe proposed treatment for the subject is generated based at least inpart on comparison to a database of previously proposed treatments ofother subjects.

Types of Plaque Composition and Non-Calcium Score Introduction

Disclosed herein are systems, methods, and devices for cardiovascularrisk and/or state assessment using image-based analyses. In someembodiments, the systems, devices, and methods are related tocardiovascular risk and/or disease and/or state assessment usingimage-based analysis of one or more regions and/or features ofnon-calcified plaque and/or calcified plaque. In some embodiments,assessment of cardiovascular risk and/or disease and/or state generatedusing the systems, methods, and devices herein can be utilized todiagnose and/or generate a proposed treatment for a patient.

As such, in some embodiments, the systems, devices, and methodsdescribed herein are able to provide physicians and/or patients specificquantified and/or measured data relating to a patient's plaque that donot exist today. For example, in some embodiments, the system canprovide a specific numerical value for the volume of stable and/orunstable plaque, the ratio thereof against the total vessel volume,percentage of stenosis, and/or the like, using for example radiodensityvalues of pixels and/or regions within a medical image. In someembodiments, such detailed level of quantified plaque parameters fromimage processing and downstream analytical results can provide moreaccurate and useful tools for assessing the health and/or risk ofpatients in completely novel ways.

As discussed herein, disclosed herein are systems, methods, and devicesfor cardiovascular risk and/or disease state assessment usingimage-based analyses. In particular, in some embodiments, the systems,devices, and methods are related to cardiovascular risk and/or diseasestate assessment using image-based analysis of one or more regionsand/or features of non-calcified plaque and/or calcified plaque. In someembodiments, assessment of cardiovascular risk and/or disease and/orstate generated using the systems, methods, and devices herein can beutilized to diagnose and/or generate a proposed treatment for a patient.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to identify and/or analyze one ormore regions of non-calcified plaque. Generally speaking, a region ofplaque can be classified as non-calcified and/or calcified. In someembodiments, the system can be configured to identify and/or classifyone or more regions of plaque as calcified and/or non-calcified. In someembodiments, within non-calcified plaque, the system can be configuredto further classify certain non-calcified plaque as low-densitynon-calcified plaque. As described herein, generally speaking,non-calcified plaque can be considered more dangerous, while calcifiedplaque can be considered more stable. In other words, in someembodiments, the system can be configured to characterize non-calcifiedplaque as unstable plaque, while calcified plaque as stable plaque, asnon-calcified plaque is the type of plaque that can rupture or causesudden myocardial infarction heart attack, whereas non-calcified plaquecan remain stable without causing a heart attack.

In some embodiments, as part of quantitative phenotyping, the system canbe configured to identify and/or characterize different types and/orregions of plaque, for example based on density, absolute density,material density, relative density, and/or radiodensity. For example, insome embodiments, the system can be configured to characterize a regionof plaque into one or more sub-types of plaque. For example, in someembodiments, the system can be configured to characterize a region ofplaque as one or more of low density non-calcified plaque, non-calcifiedplaque, or calcified plaque. In some embodiments, calcified plaque cancorrespond to plaque having a highest density range, low densitynon-calcified plaque can correspond to plaque having a lowest densityrange, and non-calcified plaque can correspond to plaque having adensity range between calcified plaque and low density non-calcifiedplaque. For example, in some embodiments, the system can be configuredto characterize a particular region of plaque as low densitynon-calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about −189 and about30 Hounsfield units (HU). In some embodiments, the system can beconfigured to characterize a particular region of plaque asnon-calcified plaque when the radiodensity of an image pixel or voxelcorresponding to that region of plaque is between about 31 and about 350HU. In some embodiments, the system can be configured to characterize aparticular region of plaque as calcified plaque when the radiodensity ofan image pixel or voxel corresponding to that region of plaque isbetween about 351 and about 2500 HU.

In some embodiments, the lower and/or upper Hounsfield unit boundarythreshold for determining whether a plaque corresponds to one or more oflow density non-calcified plaque, non-calcified plaque, and/or calcifiedplaque can be about −1000 HU, about −900 HU, about −800 HU, about −700HU, about −600 HU, about −500 HU, about −400 HU, about −300 HU, about−200 HU, about −190 HU, about −180 HU, about −170 HU, about −160 HU,about −150 HU, about −140 HU, about −130 HU, about −120 HU, about −110HU, about −100 HU, about −90 HU, about −80 HU, about −70 HU, about −60HU, about −50 HU, about −40 HU, about −30 HU, about −20 HU, about −10HU, about 0 HU, about 10 HU, about 20 HU, about 30 HU, about 40 HU,about 50 HU, about 60 HU, about 70 HU, about 80 HU, about 90 HU, about100 HU, about 110 HU, about 120 HU, about 130 HU, about 140 HU, about150 HU, about 160 HU, about 170 HU, about 180 HU, about 190 HU, about200 HU, about 210 HU, about 220 HU, about 230 HU, about 240 HU, about250 HU, about 260 HU, about 270 HU, about 280 HU, about 290 HU, about300 HU, about 310 HU, about 320 HU, about 330 HU, about 340 HU, about350 HU, about 360 HU, about 370 HU, about 380 HU, about 390 HU, about400 HU, about 410 HU, about 420 HU, about 430 HU, about 440 HU, about450 HU, about 460 HU, about 470 HU, about 480 HU, about 490 HU, about500 HU, about 510 HU, about 520 HU, about 530 HU, about 540 HU, about550 HU, about 560 HU, about 570 HU, about 580 HU, about 590 HU, about600 HU, about 700 HU, about 800 HU, about 900 HU, about 1000 HU, about1100 HU, about 1200 HU, about 1300 HU, about 1400 HU, about 1500 HU,about 1600 HU, about 1700 HU, about 1800 HU, about 1900 HU, about 2000HU, about 2100 HU, about 2200 HU, about 2300 HU, about 2400 HU, about2500 HU, about 2600 HU, about 2700 HU, about 2800 HU, about 2900 HU,about 3000 HU, about 3100 HU, about 3200 HU, about 3300 HU, about 3400HU, about 3500 HU, and/or about 4000 HU.

In some embodiments, the system can be configured to determine and/orcharacterize the burden of atherosclerosis based at least in part onvolume of plaque. In some embodiments, the system can be configured toanalyze and/or determine total volume of plaque and/or volume oflow-density non-calcified plaque, non-calcified plaque, and/or calcifiedplaque. In some embodiments, the system can be configured to performphenotyping of plaque by determining a ratio of one or more of theforegoing volumes of plaque, for example within an artery, lesion,vessel, and/or the like.

Despite the risks associated with non-calcified plaque, certaintechniques focus more on features of calcified plaque as opposed tonon-calcified plaque. For example, in some instances, calcium scores canbe used as a marker for assessing the state of cardiovascular health ordisease for a subject. In an example, a coronary artery calcium score isa determination or measurement of the amount of calcium in the walls ofthe arteries that supply the heart muscle. Traditionally, calcium scoreshave been used to estimate the risk of a heart attack or stroke, forexample, in the next 5-10 years. There are many technical shortcomings,however, of focusing solely on calcium scores. For one, a high orincreased calcium score alone is not representative of any specificcause, either positive or negative. Rather, in general, there can bevarious possible causes for a high or increased calcium score. Forexample, in some cases, a high or increased calcium score can be anindicator of significant heart disease and/or that the patient is atincreased risk of a heart attack. Also, in some cases, a high orincreased calcium score can be an indicator that the patient isincreasing the amount of exercise performed, because exercise canconvert fatty material plaque within the artery vessel. In some cases, ahigh or increased calcium score can be an indicator of the patientbeginning a statin regimen wherein the statin is converting the fattymaterial plaque into calcium. Further, even if quantitative image-basedtechniques are used, merely focusing on calcified plaque alone may notprovide an accurate assessment of state of cardiovascular disease forsimilar reasons.

As such, to address such technical shortcomings, in some embodimentsdescribed herein, the systems, devices, and methods are configured toassess cardiovascular risk and/or disease state using image-basedanalysis of one or more regions and/or features of non-calcified plaqueand/or calcified plaque.

For example, FIG. 11A is a schematic illustrating an example of one ormore regions of calcified and/or non-calcified plaque that can beanalyzed using image analysis processes by one or more embodiments ofthe systems, methods, and devices herein for assessment ofcardiovascular risk, disease, and/or state. As illustrated in FIG. 11A,in some embodiments, the systems, methods, and devices described hereincan be configured to identify one or more regions of plaque 1102, 1104within an artery or vessel 1100. In some embodiments, the one or moreregions of plaque 1102 can further comprise one or more regions and/ortypes and/or compositions of plaque 1106, 1108, 1110. In someembodiments, the one or more regions of plaque 1104 can comprise asingle type or composition of plaque. In some embodiments, a region ofplaque 1102, 1104, 1106, 1108, 1110 can be composed of one or moredifferent types or compositions of plaque. For example, in someembodiments, the system can be configured to analyze, characterize,and/or classify plaque into one of three types: (i) low-densitynon-calcified plaque, (ii) non-calcified plaque, and (iii) calcifiedplaque. In some embodiments, the system can be configured to analyze,characterize, and/or classify plaque into one of two types: (i)non-calcified plaque, and (ii) calcified plaque. In some embodiments,the system can be configured to analyze, characterize, and/or classifyplaque into one of any number of different types, such as, for example,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, and/or 30 different types ofclassifications of plaque. Accordingly, in an example, each of the oneor more regions of plaque can be determined to be composed oflow-density non-calcified plaque, non-calcified plaque, or calcifiedplaque. In another example, each of the one or more regions of plaquecan be determined to be composed of non-calcified plaque or calcifiedplaque. In another example, each of the one or more regions of plaquecan be determined to be composed of one of a number of different typespf plaque (e.g., 1, 2, 3, or more different types of plaque). In someembodiments, the system can be configured to analyze, characterize,and/or classify plaque into a number of different types within a rangesof defined by two of the aforementioned values of radiodensity of animage pixel or voxel corresponding to that region of plaque.

In some embodiments, the system can be configured to analyze,characterize, and/or classify plaque based on one or more plaqueparameters or characteristics. For example, one or more such plaqueparameters can include plaque density, radiodensity, location, volume,surface area, geometry, heterogeneity, diffusivity, or ratio betweenvolume and surface area, among others. In an example, radiodensity isdetermined by the density of information from a medical image. In anexample, plaque density is determined based on where radiodensity valuesare relative to thresholds, e.g., predetermined thresholds. In anexample, plaque location relates to one or more characterizations of theplaque location, e.g., proximal vs. middle vs. distal, myocardial vs.pericardial facing, at bifurcation or trifurcation vs. not atbifurcation or trifurcation, plaque location in main vessel vs. branchvessel, and the like. The system can determine plaque volume, surfacearea, geometry and/or ratio between volume and surface area based on thepixels or voxels identified to be plaque, or a region of plaque, in oneor more of the evaluated patient images. In some embodiments,heterogeneity of plaque, or a region of plaque, can be determined by theradiodensity values in a plaque region, for example, the consistencyand/or range of radiodensity values in a plaque region. In someembodiments, diffusivity plaque density relates to characterizing the“spread” of the density of plaque in a region. Some of the plaqueparameters are determined directly from characterization of the plaqueIn some embodiments, the system can be configured to utilize one or moreimage processing, machine learning, and/or artificial intelligencetechniques to analyze a medical image to derive one or more such plaqueparameters, which can be used to classify plaque. For example, in someembodiments, the system can be configured to classify a particularregion of plaque as non-calcified plaque and/or low-densitynon-calcified plaque if a density or radiodensity value of a pixelrepresenting plaque is lower than a predetermined threshold and/orwithin a predetermined range. Similarly, in some embodiments, the systemcan be configured to classify a particular region of plaque as calcifiedplaque if a density or radiodensity value of a pixel representing plaqueis higher than a predetermined threshold and/or within a predeterminedrange.

In some embodiments, the system can be configured to analyze a medicalimage to determine one or more other parameters, such as for examplevascular parameters and/or relational parameters between plaque and/orvascular parameters. In particular, in some embodiments, vascularparameters can include one or more of vascular volume, diameter, area,cross-sectional area, surface area, length, location, and/or remodeling(e.g., a remodeling index). In some embodiments, relational parameterscan include a ratio of surface area of plaque to surface of vessel,ratio of volume of plaque to volume of vessel, and/or a ratio ofthickness of plaque to thickness of vessel.

In some embodiments, the system can be configured to analyze one or moreplaque parameters of non-calcified and/or calcified plaque and/or anyother type or classification of plaque, one or more vascular parameters,and/or one or more relational parameters to assess a risk or state ofcardiovascular disease or health of a subject. For example, in someembodiments, the system can be configured to compare one or more plaqueparameters, one or more vascular parameters, and/or one or morerelational parameters to a reference value database comprising one ormore plaque parameters, one or more vascular parameters, and/or one ormore relational parameters derived from images of plaque of othersubjects with varying states or risks of cardiovascular disease orhealth, including for example normal values. In some embodiments, basedon such analysis of one or more plaque parameters, one or more vascularparameters, and/or one or more relational parameters, the system can beconfigured to generate an overall plaque score, non-calcified plaquescore, non-calcium score, calcified plaque score, and/or calcium score.In some embodiments, the system can be configured to generate an overallplaque score, non-calcified plaque score, non-calcium score, calcifiedplaque score, and/or calcium score directly from one or more plaqueparameters one or more vascular parameters, and/or one or morerelational parameters. In some embodiments, the system can be configuredto compare the generated overall plaque score, non-calcified plaquescore, non-calcium score, calcified plaque score, and/or calcium scoreto a reference values database comprising one or more overall plaquescores, non-calcified plaque scores, non-calcium scores, calcifiedplaque scores, and/or calcium scores derived from a population ofsubjects with varying states of cardiovascular disease and/or health,including for example normal values.

In some embodiments, the system can be configured to assess the riskand/or state of cardiovascular disease or health based on the generatedoverall plaque score, non-calcified plaque score, non-calcium score,calcified plaque score, and/or calcium score and/or comparison thereofto a reference values database, including for example one or morereference values such as normal values. In some embodiments, a currentpatient's risk and/or disease state for a subject can be based in parton one or more of an age, gender, race, systolic blood pressure,diastolic blood pressure, total cholesterol, high-density lipoprotein(HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, history ofdiabetes, whether a current or past smoker, hypertension, currentmedication, or an ASCVD risk score. In some embodiments, a current'spatient's information relating to one or more of age, gender, race,systolic blood pressure, diastolic blood pressure, total cholesterol,high-density lipoprotein (HDL) cholesterol, low-density lipoprotein(LDL) cholesterol, history of diabetes, smoking history, hypertension,current medication, and/or an ASCVD risk score can be used to assess therisk and/or cardiovascular disease or health in addition to their plaqueinformation. In some embodiments, in addition to their plaqueinformation, a current's patient's information relating to one or moreof age, gender, race, systolic blood pressure, diastolic blood pressure,total cholesterol, high-density lipoprotein (HDL) cholesterol,low-density lipoprotein (LDL) cholesterol, history of diabetes, smokinghistory, hypertension, current medication, and/or an ASCVD risk scorecan be compared to this information of other patients, and/or also theiroutcomes, to assess the risk and/or cardiovascular disease or health oftheir that is compared to other patients' ASCVD risk scores and outcomes(e.g., that are stored in a database). In some embodiments, the systemcan be configured to determine or generate a proposed treatment for thesubject based on the assessed risk and/or state of cardiovasculardisease or health. For example, the proposed treatment can include oneor more of medical therapy (such as statins), interventional therapy(such as stent implantation), and/or lifestyle therapy (such as diet orexercise). In some embodiments, the system can be configured to trackthe efficacy of a treatment by tracking changes in the overall plaquescore, non-calcified plaque score, non-calcium score, calcified plaquescore, and/or calcium score, for example compared to a previouslygenerated score for the same subject and/or change relative to areference values database comprising one or more reference values, suchas for example normal values.

As such, in some embodiments, the systems, devices, and methodsdescribed herein provide a quantitative and/or image-based solution forgenerating and/or tracking cardiovascular disease or health by analyzingone or more features of non-calcified and/or calcified plaques.

Cardiovascular Risk and/or Disease State Assessment Using Image-BasedAnalysis of Calcified and/or Non-Calcified Plaque Features

FIG. 11B is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for cardiovascular risk and/or diseasestate assessment using image-based analyses of non-calcified and/orcalcified plaque.

As illustrated in FIG. 11B, in some embodiments, the system can beconfigured to access a medical image at block 1122. In some embodiments,the medical image can include one or more arteries, such as coronary,carotid, and/or other arteries of a subject. In some embodiments, themedical image can be stored in a medical image database 1124. In someembodiments, the medical image database 1124 can be locally accessibleby the system and/or can be located remotely and accessible through anetwork connection. The medical image can comprise an image obtainedusing one or more modalities, for example, CT, Dual-Energy ComputedTomography (DECT), Spectral CT, photon-counting CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), Magnetic Resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).In some embodiments, the medical image comprises one or more of acontrast-enhanced CT image, non-contrast CT image, MR image, and/or animage obtained using any of the modalities described above.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 1126, thesystem can be configured to identify and/or characterize one or morevessels and/or lumen, such as of one or more arteries. The one or morearteries can include coronary arteries, carotid arteries, aorta, renalartery, lower extremity artery, upper extremity artery, and/or cerebralartery, amongst others. In some embodiments, the system can beconfigured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically identify one or more arteries orcoronary arteries using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich arteries or coronary arteries have been identified, therebyallowing the AI and/or ML algorithm automatically identify arteries orcoronary arteries directly from a medical image. In some embodiments,the arteries or coronary arteries are identified by size and/orlocation.

In some embodiments, at block 1128, the system can be configured toidentify and/or determine one or more vascular parameters. In someembodiments, vascular parameters can include one or more of vascularvolume, diameter, area, cross-sectional area, surface area, length,location, and/or remodeling. In some embodiments, the system can beconfigured to analyze the one or more vascular parameters at block 1130.For example, in some embodiments, the system can be configured tocompare the one or more vascular parameters derived from the image toone or more reference values of vascular parameters, such as for examplenormal values. The one or more reference values of vascular parameterscan be stored on a reference values database 1132, which can be locallyaccessible by the system and/or can be located remotely and accessiblethrough a network connection.

In some embodiments, at block 1134, the system can be configured toidentify and/or characterize one or more regions of plaque within themedical image. In some embodiments, the system can be configured toutilize one or more AI and/or ML algorithms to automatically and/ordynamically identify and/or characterize one or more regions of plaqueusing image processing. For example, in some embodiments, the one ormore AI and/or ML algorithms can be trained using a Convolutional NeuralNetwork (CNN) on a set of medical images on which one or more regions ofplaque have been identified and/or characterized. The set of medicalimages used for training the AI and/or ML algorithms ideally includes alarge number of images in which regions of plaque have been identifiedand/or characterized. For example, at least hundreds of images, and morepreferably thousands of images, tens of thousands images, hundreds ofthousands of images, or more than hundreds of thousands of images (e.g.,millions, tens of millions or more). The number of images used to trainan AI and/or ML algorithm can increase over time to improve the AIand/or ML algorithm and increase the accuracy of the identification ofregions of plaque and increase the accuracy of the characterization ofregions of plaque. When trained, the AI and/or ML algorithm canautomatically identify and/or characterize regions of plaque directlyfrom a medical image of a subject based on using information in one ormore images of the subject, for example, using a neural network trainedfor feature recognition and/or classification, or another type of AI orML algorithm.

For example, in some embodiments, the system can be configured toanalyze the density and/or radiodensity values and/or heterogeneity ordistribution thereof of one or more pixels on a medical image toidentify plaque and/or characterize plaque. More specifically, in someembodiments, the system can be configured to identify a particular pixelin an image of a patient as plaque, non-calcified plaque, low-densitynon-calcified plaque, and/or calcified plaque, and/or any otherclassification or type of plaque. In some embodiments, the system can beconfigured to classify a particular pixel or region of plaque asnon-calcified plaque when the radiodensity value of the pixel is below acertain predetermined threshold, within a predetermined range, and/orcomprises a heterogeneity index or distribution within, under, or abovea particular predetermined range or threshold. In some embodiments, thesystem can be configured to classify a particular pixel or region ofplaque as low-density non-calcified plaque when the radiodensity valueof the pixel is below a certain predetermined threshold, within apredetermined range, and/or comprises a heterogeneity index ordistribution within, under, or above a particular predetermined range orthreshold. In some embodiments, the system can be configured to classifya particular pixel or region of plaque as calcified plaque when theradiodensity value of the pixel is above a certain predeterminedthreshold, within a predetermined range, and/or comprises aheterogeneity index or distribution within, under, or above a particularpredetermined range or threshold.

In some embodiments, at block 1136, the system can be configured todetermine whether a region of plaque, and/or a particular pixelcorresponding to plaque, on the medical image should be classified ascalcified or non-calcified plaque. This can be done, for example, basedon one or more criteria and/or features and/or techniques discussedherein, including for example AI, ML, imaging processing, and/or thelike.

In some embodiments, the system can be configured to utilize a CT orother medical image of a subject as input for performing one or moreimage analysis techniques to assess a subject, including for exampleassessing cardiovascular disease risk based on determining a patient'scalcified or non-calcified plaque. In particular, by determining a valueof non-calcified plaque which may be more indicative of cardiovasculardisease risk. Non-calcified plaque is a low attenuation plaque relativeto calcified plaque. In some embodiments, such CT image can comprise acontrast-enhanced CT image, in which case some of the analysistechniques described herein can be directly applied, for example toidentify or classify plaque. In some embodiments, such CT image cancomprise a non-contrast CT image, in which case it can be more difficultto identify and/or determine non-calcified plaque due to its lowradiodensity value and overlap with other low radiodensity valuescomponents, such as blood for example. In some embodiments, these “lowattenuation” plaques may be differentiated between the blood attenuationdensity and the fat that sometimes surrounds the coronary artery and/ormay represent non-calcified plaques of different materials. In someembodiments, the presence of these non-calcified plaques may offerincremental prediction for whether a previously calcified plaque isstabilizing or worsening or progressing or regressing. These findingsthat are measurable through these embodiments may be linked to theprognosis of a patient, wherein calcium stabilization (that is, higherattenuation densities) and lack of non-calcified plaque by mayassociated with a favorable prognosis, while lack of calciumstabilization (that is, no increase in attenuation densities), orsignificant progression or new calcium formation may be associated witha poorer prognosis, including risk of rapid progression of disease,heart attack or other major adverse cardiovascular event.

A quantitative variable that is used in the system and displayed onvarious portions of a user interface of the system in reference tolow-density non-calcified plaque, non-calcified plaque, and calcifiedplaque, is the Hounsfield unit (HU). As is known, a Hounsfield Unitscale is a quantitative scale for describing radiation, and isfrequently used in reference to CT scans as a way to characterizeradiation attenuation and thus making it easier to define what a givenfinding may represent. A Hounsfield Unit measurement is presented inreference to a quantitative scale. Examples of Hounsfield Unitmeasurements of certain materials are shown in the following table:

Material HU Air −1000 Fat −50 Distilled Water 0 Soft Tissue +40 Blood+40 to 80 Calcified Plaques 350-1000+ Bone +1000

In some embodiments, to determine non-calcified and/or low-attenuatedplaque from the medical image or non-contrast CT image, the system canbe configured to utilize a stepwise approach to first identify areaswithin the medical image that are clearly non-calcified plaque. In someembodiments, the system can then conduct a more detailed analysis of theremaining areas in the image to identify other regions of non-calcifiedand/or low-attenuated plaque. By utilizing such compartmentalized or astepwise approach, in some embodiments, the system can identify ordetermine non-calcified and/or low-attenuated plaque from the medicalimage or non-contrast CT image with a faster turnaround rather thanhaving to apply a more complicated analysis to every region or pixel ofthe image. In some embodiments, predetermined thresholds ofradiodensities from a CT image(s) may be used to differentiate plaquetypes, for example, to differentiate low-density non-calcified plaque(LD-NCP), non-calcified plaque (NCP) and calcified plaque (CP) plaque.In an example for a contrast CT image, LD-NCP is in the range of about−189 to 30 Hounsfield Units (HU), NCP is in the range of about −189 to350 HU, and CP is in the range of about 350 to 2500 HU. Default orpredetermined values can be revised, if desired, for example, using aPlaque Threshold interface displayable on the system. Although defaultvalues are provided, users can select different plaque thresholds basedon their clinical judgment.

Information pertaining to the length and volume can be determined for avessel along with plaque and other information (e.g., stenosisinformation) on a per-vessel and per-lesion level. Users may excludeartifacts from the image they do not want to be considered in thecalculations by using the exclusion tool. The following tables indicatecertain statistics that are available for vessels, lesions, plaque, andstenosis.

Vessel

Term Definition Vessel Length Length of a linear coronary vessel (mm)Total Vessel Volume The volume of consecutive slices of (mm³) vesselcontours. Total Lumen Volume The volume of consecutive slices of (mm³)lumen contours

Lesion

Term Definition Lesion Length Linear distance from the start of acoronary (mm) lesion to the end of a coronary lesion. Vessel Volume Thevolume of consecutive slices of vessel (mm³) contours. Lumen Volume Thevolume of consecutive slices of lumen (mm³) contours.

Plaque

Term Definition Total Calcified Plaque Calcified plaque is defined asplaque in Volume (mm³) between the lumen and vessel wall with anattenuation of greater than 350 HU, or as defined by the user, and isreported in absolute measures by plaque volume. Calcified plaques can beidentified in each coronary artery ≥1.5 mm in mean vessel diameter.Total Non-Calcified Plaque Non-calcified plaque is defined as plaque inVolume (mm³) between the lumen and vessel wall with an attenuation ofless than or equal to 350, or as defined by the user, HU and is reportedin absolute measures by plaque volume. The total non-calcified plaquevolume can be determined to be the sum total of all non- calcifiedplaques identified in each coronary artery ≥1.5 mm in mean vesseldiameter. Non-calcified plaque data reported is further broken down intolow-density plaque, based on HU density thresholds. Low-DensityNon-Calcified Low-Density--Non-Calcified Plaque is Plaque Volume definedas plaque in between the lumen and (mm³) vessel wall with an attenuationof less than or equal to 30 HU or as defined by the user and is reportedin absolute measures by plaque volume. Total Plaque Volume (mm³) Plaquevolume is defined as plaque in between the lumen and vessel wallreported in absolute measures. The total plaque volume is the sum totalof all plaque identified in each coronary artery ≥1.5 mm in mean vesseldiameter or wherever the user places the ″End″ marker.

Stenosis

Term Definition Remodeling Index Remodeling Index is defined as the meanvessel diameter at a denoted slice divided by the mean vessel diameterat a reference slice. Greatest Diameter The deviation of the mean lumendiameter at Stenosis (%) the denoted slice from a reference slice,expressed in percentage. Greatest Area The deviation of the lumen areaat the Stenosis (%) denoted slice to a reference area, expressed inpercentage

The system can be configured to identify epicardial fat from the medicalimage. In some embodiments, the system can be configured to identifyepicardial fat by determining every pixel or region within the imagethat has a radiodensity value below a predetermined threshold and/orwithin a predetermined range. The exact predetermined threshold value orrange of radiodensity for identifying epicardial fat can depend on themedical image, scanner type, scan parameters, and/or the like, which iswhy a normalization device can be used in some instances to normalizethe medical image. For example, in some embodiments, the system can beconfigured to identify as epicardial fat pixels and/or regions withinthe medical image or non-contrast CT image with a radiodensity valuethat is around −100 Hounsfield units and/or within a range that includes−100 Hounsfield units. In particular, in some embodiments, the systemcan be configured to identify as epicardial fat pixels and/or regionswithin the medical image or non-contrast CT image with a radiodensityvalue that is within a range with a lower limit of about −100 Hounsfieldunits, about −110 Hounsfield units, about −120 Hounsfield units, about−130 Hounsfield units, about −140 Hounsfield units, about −150Hounsfield units, about −160 Hounsfield units, about −170 Hounsfieldunits, about −180 Hounsfield units, about −190 Hounsfield units, orabout −200 Hounsfield units, and an upper limit of about 30 Hounsfieldunits, about 20 Hounsfield units, about 10 Hounsfield units, aboutHounsfield units, about −10 Hounsfield units, about −20 Hounsfieldunits, about −30 Hounsfield units, about −40 Hounsfield units, about −50Hounsfield units, about −60 Hounsfield units, about −70 Hounsfieldunits, about −80 Hounsfield units, or about −90 Hounsfield units.

In some embodiments, the system can be configured to identify and/orsegment arteries on the medical image or non-contrast CT image using theidentified epicardial fat as outer boundaries of the arteries. Forexample, the system can be configured to first identify regions ofepicardial fat on the medical image and assign a volume in betweenepicardial fat as an artery, such as a coronary artery.

In some embodiments, the system can be configured to identify a firstset of pixels or regions within the medical image, such as within theidentified arteries, as non-calcified or low-attenuated plaque. Morespecifically, in some embodiments, the system can be configured toidentify as an initial set low-attenuated or non-calcified plaque byidentifying pixels or regions with a radiodensity value that is below apredetermined threshold or within a predetermined range. For example,the predetermined threshold or predetermined range can be set such thatthe resulting pixels can be confidently marked as low-attenuated ornon-calcified plaque without likelihood of confusion with another mattersuch as blood. In particular, in some embodiments, the system can beconfigured to identify the initial set of low-attenuated ornon-calcified plaque by identifying pixels or regions with aradiodensity value below around Hounsfield units. In some embodiments,the system can be configured to identify the initial set oflow-attenuated or non-calcified plaque by identifying pixels or regionswith a radiodensity value at or below around 60 Hounsfield units, around55 Hounsfield units, around Hounsfield units, around 45 Hounsfieldunits, around 40 Hounsfield units, around 35 Hounsfield units, around 30Hounsfield units, around 25 Hounsfield units, around 20 Hounsfieldunits, around 15 Hounsfield units, around 10 Hounsfield units, around 5Hounsfield units, and/or with a radiodensity value at or above around 0Hounsfield units, around 5 Hounsfield units, around 10 Hounsfield units,around 15 Hounsfield units, around 20 Hounsfield units, around 25Hounsfield units, and/or around 30 Hounsfield units. In someembodiments, the system can be configured to classify pixels or regionsthat fall within or below this predetermined range of radiodensityvalues as a first set of identified non-calcified or low-attenuatedplaque.

In some embodiments, the system can be configured to identify a secondset of pixels or regions within the medical image, such as within theidentified arteries, that may or may not represent low-attenuated ornon-calcified plaque. This second set of candidates of pixels or regionsmay require additional analysis to confirm that they represent plaque.In particular, in some embodiments, the system can be configured toidentify this second set of pixels or regions that may potentially below-attenuated or non-calcified plaque by identifying pixels or regionsof the image with a radiodensity value within a predetermined range. Insome embodiments, the predetermined range for identifying this secondset of pixels or regions can be between around 30 Hounsfield units and100 Hounsfield units. In some embodiments, the predetermined range foridentifying this second set of pixels or regions can have a lower limitof around 0 Hounsfield units, 5 Hounsfield units, 10 Hounsfield units,15 Hounsfield units, 20 Hounsfield units, 25 Hounsfield units, 30Hounsfield units, 35 Hounsfield units, 40 Hounsfield units, 45Hounsfield units, 50 Hounsfield units, and/or an upper limit of around55 Hounsfield units, 60 Hounsfield units, 65 Hounsfield units, 70Hounsfield units, 75 Hounsfield units, 80 Hounsfield units, 85Hounsfield units, 90 Hounsfield units, 95 Hounsfield units, 100Hounsfield units, 110 Hounsfield units, 120 Hounsfield units, 130Hounsfield units, 140 Hounsfield units, 150 Hounsfield units.

In some embodiments, the system can be configured to conduct an analysisof the heterogeneity of the identified second set of pixels or regions.For example, depending on the range of radiodensity values used toidentify the second set of pixels, in some embodiments, the second setof pixels or regions may include blood and/or plaque. Blood cantypically show a more homogeneous gradient of radiodensity valuescompared to plaque. As such, in some embodiments, by analyzing thehomogeneity or heterogeneity of the pixels or regions identified as partof the second set, the system can be able to distinguish between bloodand non-calcified or low attenuated plaque. As such, in someembodiments, the system can be configured to determine a heterogeneityindex of the second set of regions of pixels identified from the medicalimage by generating spatial mapping, such as a three-dimensionalhistogram, of radiodensity values within or across a geometric shape orregion of plaque. In some embodiments, if a gradient or change inradiodensity values across the spatial mapping is above a certainthreshold, the system can be configured to assign a high heterogeneityindex and/or classify as plaque. Conversely, in some embodiments, if agradient or change in radiodensity values across the spatial mapping isbelow a certain threshold, the system can be configured to assign a lowheterogeneity index and/or classify as blood.

In some embodiments, the system can be configured to identify a subsetof the second set of regions of pixels identified from the medical imageas plaque or non-calcified or low-attenuated plaque. In someembodiments, the system can be configured to combine the first set ofidentified non-calcified or low-attenuated plaque and the second set ofidentified non-calcified or low-attenuated plaque. As such, even usingnon-contrast CT images, in some embodiments, the system can beconfigured to identify low-attenuated or non-calcified plaque which canbe more difficult to identify compared to calcified or high-attenuatedplaque due to possible overlap with other matter such as blood.

In some embodiments, the system can also be configured to determinecalcified or high-attenuated plaque from the medical image. This processcan be more straightforward compared to identifying low-attenuated ornon-calcified plaque from the medical image or non-contrast CT image. Inparticular, in some embodiments, the system can be configured toidentify calcified or high-attenuated plaque from the medical image ornon-contrast CT image by identifying pixels or regions within the imagethat have a radiodensity value above a predetermined threshold and/orwithin a predetermined range. For example, in some embodiments, thesystem can be configured to identify as calcified or high-attenuatedplaque regions or pixels from the medical image or non-contrast CT imagehaving a radiodensity value above around 100 Hounsfield units, around150 Hounsfield units, around 200 Hounsfield units, around 250 Hounsfieldunits, around 300 Hounsfield units, around 350 Hounsfield units, around400 Hounsfield units, around 450 Hounsfield units, around 500 Hounsfieldunits, around 600 Hounsfield units, around 700 Hounsfield units, around800 Hounsfield units, around 900 Hounsfield units, around 1000Hounsfield units, around 1100 Hounsfield units, around 1200 Hounsfieldunits, around 1300 Hounsfield units, around 1400 Hounsfield units,around 1500 Hounsfield units, around 1600 Hounsfield units, around 1700Hounsfield units, around 1800 Hounsfield units, around 1900 Hounsfieldunits, around 2000 Hounsfield units, around 2500 Hounsfield units,around 3000 Hounsfield units, and/or any other minimum threshold.

In some embodiments, the system can be configured to generate aquantized color mapping of one or more identified matters from themedical image. For example, in some embodiments, the system can beconfigured assign different colors to each of the different regionsassociated with different matters, such as non-calcified orlow-attenuated plaque, calcified or high-attenuated plaque, all plaque,arteries, epicardial fat, and/or the like. In some embodiments, thesystem can be configured to generate a visualization of the quantizedcolor map and/or present the same to a medical personnel or patient viaa GUI. In some embodiments, at block 236, the system can be configuredto generate a proposed treatment plan for a disease based on one or moreof the identified non-calcified or low-attenuated plaque, calcified orhigh-attenuated plaque, all plaque, arteries, epicardial fat, and/or thelike. For example, in some embodiments, the system can be configured togenerate a treatment plan for an arterial disease, renal artery disease,abdominal atherosclerosis, carotid atherosclerosis, and/or the like, andthe medical image being analyzed can be taken from any one or moreregions of the subject for such disease analysis.

In some embodiments, one or more processes described herein can berepeated. For example, if a medical image of the same subject is takenagain at a later point in time, one or more processes described hereincan be repeated and the analytical results thereof can be used fordisease tracking and/or other purposes.

Further, in some embodiments, the system can be configured to identifyand/or determine non-calcified plaque from a DECT or spectral CT image.Similar to the processes described above, in some embodiments, thesystem can be configured to access a DECT or spectral CT image, identifyepicardial fat on the DECT image or spectral CT and/or segment one ormore arteries on the DECT image or spectral CT, identify and/or classifya first set of pixels or regions within the arteries as a first set oflow-attenuated or non-calcified plaque, and/or identify a second set ofpixels or regions within the arteries as a second set of low-attenuatedor non-calcified plaque. However, unlike the techniques described above,in some embodiments, such as for example where a DECT or spectral CTimage is being analyzed, the system can be configured to identify asubset of those second set of pixels without having to perform aheterogeneity and/or homogeneity analysis of the second set of pixels.Rather, in some embodiments, the system can be configured to distinguishbetween blood and low-attenuated or non-calcified plaque directly fromthe image, for example by utilizing the dual or multispectral aspect ofa DECT or spectral CT image. In some embodiments, the system can beconfigured to combine the first set of identified pixels or regions andthe subset of the second set of pixels or regions identified aslow-attenuated or non-calcified plaque to identify a whole set of thesame on the medical image. In some embodiments, even if analyzing a DECTor spectral CT image, the system can be configured to further analyzethe second set of pixels or regions by performing a heterogeneity orhomogeneity analysis, similar to that described above. For example, evenif analyzing a DECT or spectral CT image, in some embodiments, thedistinction between certain areas of blood and/or low-attenuated ornon-calcified plaque may not be complete and/or accurate.

In some embodiments, if the system determines a particular region ofplaque to be non-calcified plaque, at block 1138, the system can beconfigured to determine one or more non-calcified plaque parametersand/or one or more relational parameters. For example, in someembodiments, one or more non-calcified plaque parameters can includeplaque density, radiodensity, location, volume, surface area, geometry,heterogeneity, diffusivity, or ratio between volume and surface area,among others. In some embodiments, one or more relational parameters caninclude a relationship between any one or more non-calcified plaqueparameters and vascular parameters. For example, in some embodiments,one or more relational parameters can include a ratio of surface area ofplaque to surface of vessel or lumen, ratio of volume of plaque tovolume of vessel or lumen, and/or the like. In some embodiments, thesystem can be configured to utilize one or more AI and/or ML algorithmsto automatically and/or dynamically determine one or more non-calcifiedplaque parameters and/or relational parameters using image processing.For example, in some embodiments, the one or more AI and/or MLalgorithms can be trained using a Convolutional Neural Network (CNN) ona set of medical images on which one or more non-calcified plaqueparameters and/or relational parameters have been determined, therebyallowing the AI and/or ML algorithm to automatically determine one ormore non-calcified plaque parameters and/or relational parametersdirectly from a medical image.

In some embodiments, at block 1140, the system can be configured toanalyze one or more of the non-calcified plaque parameters and/orrelational parameters, for example by comparing to one or more referencevalues of the same, such as normal values. More specifically, in someembodiments, the system can be configured to access a reference valuesdatabase 1142 that includes one or more non-calcified plaque parameterand/or relational parameters values derived from other subjects withvarying states or risks of cardiovascular disease, including for examplenormal values. The one or more reference values of non-calcified plaqueparameters and/or relational parameters can be stored on a referencevalues database 1142, which can be locally accessible by the systemand/or can be located remotely and accessible through a networkconnection.

In some embodiments, based on such analysis and/or comparison, thesystem, at block 1144, can be configured to generate a non-calciumscore. In some embodiments, the system can be configured to utilize oneor more AI and/or ML algorithms to automatically and/or dynamicallygenerate a non-calcium score. In some embodiments, the non-calcium scorecan be for the whole subject, for a particular vessel, for a particularlesion, for a particular artery, and/or the like.

In some embodiments, if the system determines a particular region ofplaque to be calcified plaque, at block 1146, the system can beconfigured to determine one or more calcified plaque parameters and/orone or more relational parameters. For example, in some embodiments, oneor more calcified plaque parameters can include plaque density,radiodensity, location, volume, surface area, geometry, heterogeneity,diffusivity, or ratio between volume and surface area, among others. Insome embodiments, one or more relational parameters can include arelationship between any one or more calcified plaque parameters andvascular parameters. For example, in some embodiments, one or morerelational parameters can include a ratio of surface area of plaque tosurface of vessel or lumen, ratio of volume of plaque to volume ofvessel or lumen, and/or the like. In some embodiments, the system can beconfigured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically determine one or more calcified plaqueparameters and/or relational parameters using image processing. Forexample, in some embodiments, the one or more AI and/or ML algorithmscan be trained using a Convolutional Neural Network (CNN) on a set ofmedical images on which one or more calcified plaque parameters and/orrelational parameters have been determined, thereby allowing the AIand/or ML algorithm to automatically determine one or more calcifiedplaque parameters and/or relational parameters directly from a medicalimage.

In some embodiments, at block 1148, the system can be configured toanalyze one or more of the calcified plaque parameters and/or relationalparameters, for example by comparing to one or more reference values ofthe same, which may include normal values. More specifically, in someembodiments, the system can be configured to access a reference valuesdatabase 1142 that includes one or more calcified plaque parameterand/or relational parameters values derived from other subjects withvarying states or risks of cardiovascular disease, including for examplenormal values. The one or more reference values of calcified plaqueparameters and/or relational parameters can be stored on a referencevalues database 1142, which can be locally accessible by the systemand/or can be located remotely and accessible through a networkconnection.

In some embodiments, based on such analysis and/or comparison, thesystem, at block 1150, can be configured to generate a calcium score. Insome embodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically generate acalcium score. In some embodiments, the calcium score can be for thewhole subject, for a particular vessel, for a particular lesion, for aparticular artery, and/or the like.

In some embodiments, at block 1152, the system can be configured togenerate one or more weighted measures of the non-calcium score and/orcalcium score. For example, in some embodiments, the system can beconfigured to generate weighted measures of the non-calcium score and/orcalcium score for the subject, for a particular vessel, for a particularlesion, for a particular artery, and/or the like. In some embodiments,the system can be configured to assign a weight to the calcium scoreand/or non-calcium score in generating the weighted measure. Forexample, the system can be configured to assign a weight between 0 and 1to the calcium score and/or non-calcium score. In some embodiments, thesystem can be configured to weight the calcium score and/or non-calciumscore 0, thereby ignoring its effect. In some embodiments, the systemcan be configured to weight the calcium score and/or non-calcium score1, thereby focusing exclusively on either the calcium score or thenon-calcium score. In some embodiments, the weighted measure can alsoinclude one or more other factors or features, such as for example, age,weight, gender, plaque volume, plaque composition, vascular remodeling,high-risk plaque, lumen volume, plaque location (proximal v. middle v.distal), plaque location (myocardial v. pericardial facing), plaquelocation (at bifurcation or trifurcation v. not at bifurcation ortrifurcation), plaque location (in main vessel v. branch vessel),stenosis severity, percentage coronary blood volume, percentagefractional myocardial mass, percentile for age and/or gender, constantor other correction factor to allow for control of within-person,within-vessel, inter-plaque, plaque-myocardial relationships, and/or thelike.

In some embodiments, at block 1154, the system can be configured toanalyze the weighted measure of non-calcium score and/or calcium score,for example by comparing to one or more reference values of the same,which may include normal values. More specifically, in some embodiments,the system can be configured to access a reference values database 1156that includes one or more weighted measures of non-calcium and/orcalcium scores derived from other subjects with varying states or risksof cardiovascular disease, including for example normal values. The oneor more reference values of weighed measures of calcium scores and/ornon-calcium scores can be stored on a reference values database 236,which can be locally accessible by the system and/or can be locatedremotely and accessible through a network connection.

In some embodiments, the reference values databases 1132, 1142, 1656and/or any portion or component thereof can be part of a singledatabase. In some embodiments, the reference values databases 1132,1142, 1156 and/or any portion or component thereof can comprise separatedatabases. In some embodiments, the reference values stored on one ormore such reference values databases 1132, 1142, 1156 can be curated,triaged, filtered, or selected based on age, gender, pre-existingmedical condition, and/or the like to match the subject for improvedaccuracy. In an example, when a query is made to the reference valuedatabase, one or more characteristics of the subject (e.g., age, gender,race, pre-existing medical condition, whether a smoker, fitness level,and/or the like) may be provided (or associated) with the query, and thereference value(s) selected from the reference values database can bebased at least in part, on the provided (or associated) characteristicof the subject.

In some embodiments, based on such analysis and/or comparison, thesystem, at block 1152, can be configured to determine a risk or state ofcardiovascular disease or health of the subject. Further, in someembodiments, based on such analysis and/or comparison, the system, atblock 1158, can be configured to determine a proposed treatment for thesubject. The treatment can include, for example, medical treatment suchas statins, interventional treatment such as stent implantation, and/orlifestyle treatment such as exercise or diet. In some embodiments, indetermining the risk or state of cardiovascular disease or health and/ortreatment, the system can access a plaque risk/treatment database 1140,which can be locally accessible by the system and/or can be locatedremotely and accessible through a network connection. In someembodiments, the plaque risk/treatment database 1140 can includereference points or data that relate one or more treatment tocardiovascular disease risk or state determined based on one or moreweighted measures of non-calcium and/or calcium scores.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1122-1158 of FIG. 11B, forexample, for one or more other regions of plaque and/or other subjectsand/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease trackingand/or personalized treatment for a subject.

FIG. 11C is also a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for cardiovascular risk and/or diseasestate assessment using image-based analyses of non-calcified and/orcalcified plaque. The same reference numbers in FIGS. 11C and 11Drepresent similar features and can include any of the features describedin reference to either figure.

As illustrated in FIG. 11C, in some embodiments, the system can beconfigured to generate a non-calcium score and/or calcium score directlyfrom the determined one or more non-calcified plaque parameters,calcified plaque parameters, and/or relational parameters, withoutreferencing reference values and/or normal values. Rather, in someembodiments, the system can be configured to reference values of anon-calcium score and/or calcium score after generating the same basedon image processing techniques.

More specifically, in some embodiments, at block 1140, the system can beconfigured to generate a non-calcium score directly from one or morenon-calcified plaque parameters and/or relational parameters withoutobtaining any information from a reference values database. In someembodiments, the system can be configured to automatically and/ordynamically generate a non-calcium score. In an example, the system canutilize one or more AI and/or ML algorithms to automatically and/ordynamically generate a non-calcium score. In some embodiments, thenon-calcium score can be representative of the whole subject. In anexample, the non-calcium score can be for the whole subject where two ormore areas of the subject are evaluated to determine the non-calciumscore, although in some examples the determination of a non-calciumscore of a subject can be based on an evaluation of one area of thesubject, for example, an artery or region of a vessel that correlateswith a non-calcium value of the entire subject. In some embodiments, thenon-calcium score can be representative of a particular vessel or aportion of a vessel, the non-calcium score can be representative of aparticular lesion, and/or the non-calcium score can be representative ofa particular artery.

In some embodiments, at block 1144, after generating a non-calciumscore, the system can be configured to analyze the non-calcium score bycomparison to one or more reference values of non-calcium scores, suchas for example normal values. More specifically, in some embodiments,the system can be configured to access a reference values database 1142that includes one or more non-calcium score values derived from othersubjects with varying states or risks of cardiovascular disease. The oneor more reference values of non-calcium scores can be stored on areference values database 1142, which can be locally accessible by thesystem and/or can be located remotely and accessible through a networkconnection.

Similarly, in some embodiments, at block 1148, the system can beconfigured to generate a calcium score directly from one or morecalcified plaque parameters and/or relational parameters withoutreferencing a reference values database for the same. In someembodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically generate acalcium score. In some embodiments, the calcium score can be for thewhole subject, for a particular vessel, for a particular lesion, for aparticular artery, and/or the like.

In some embodiments, at block 1150, after generating a calcium score,the system can be configured to analyze the calcium score by comparisonto one or more reference values of calcium scores, which may includenormal values. More specifically, in some embodiments, the system can beconfigured to access a reference values database 1143 that includes oneor more calcium score values derived from other subjects with varyingstates or risks of cardiovascular disease, such as for example normalvalues. The one or more reference values of calcium scores can be storedon a reference values database 1143, which can be locally accessible bythe system and/or can be located remotely and accessible through anetwork connection.

In some embodiments, the reference values databases 1132, 1142, 1156,1143 and/or any portion or component thereof can be part of a singledatabase. In some embodiments, the reference values databases 1132,1142, 1156, 1143 and/or any portion or component thereof can compriseseparate databases. In some embodiments, the reference values stored onone or more such reference values databases 1132, 1142, 1156, 1143 canbe curated, triaged, filtered, or selected based on age, gender,pre-existing medical condition, and/or the like to match the subject forimproved accuracy.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1122-1150 in FIG. 11C, forexample for one or more other regions of plaque and/or other subjectsand/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease tracking(e.g., disease tracking over a period of time) and/or personalizedtreatment for a subject.

Cardiovascular Risk and/or Disease State Assessment Using Image-BasedAnalysis of Non-Calcified Plaque Features

FIG. 11D is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for cardiovascular risk and/or diseasestate assessment using image-based analyses of non-calcified plaque. Thesame reference numbers in FIGS. 11B, 11C, and 11D represent similarfeatures and can include any of the features described in reference toeither figure.

As illustrated in FIG. 11D, in some embodiments, the system can beconfigured to generate a non-calcium score and use the non-calcium scorealone to determine a risk or state of cardiovascular disease or healthof a subject and/or propose a treatment. As discussed herein,non-calcified plaque can be more indicative of cardiovascular riskcompared to calcified plaque. As such, a non-calcium score derived fromone or more features of non-calcified plaque and/or vascular parametersand/or relational parameters can be strong indicator of cardiovascularrisk.

More specifically, as illustrated in FIG. 11D, in some embodiments, thesystem can be configured to focus on analyzing one or more features ofnon-calcified plaque. In particular, in some embodiments, at block 1133,the system can be configured to analyze a medical image to identifyand/or characterize one or more regions of non-calcified plaque, with orwithout analyzing one or more regions of calcified plaque.

In some embodiments, based on a non-calcium score generated and/orderived from comparing one or more non-calcified plaque parametersand/or relational parameters to one or more reference values, the systemcan be configured to determine the risk and/or state of cardiovasculardisease or state of the subject at block 1160. Further, in someembodiments, based on a non-calcium score generated and/or derived fromcomparing one or more non-calcified plaque parameters and/or relationalparameters to one or more reference values, the system, at block 1104,can be configured to determine a proposed treatment for the subject. Thetreatment can include, for example, medical treatment such as statins,interventional treatment such as stent implantation, and/or lifestyletreatment such as exercise or diet. In some embodiments, in determiningthe risk or state of cardiovascular disease or health and/or treatment,the system can access a plaque risk/treatment database 306, which can belocally accessible by the system and/or can be located remotely andaccessible through a network connection. In some embodiments, the plaquerisk/treatment database 306 can include reference points or data thatrelate one or more treatment to cardiovascular disease risk or statedetermined based on non-calcium scores.

In some embodiments, the databases and/or any portion or componentthereof can be part of a single database. In an example, referencevalues databases 1132, 1142, 1156, 1143 and/or any portion or componentthereof can be part of a single database. In another example, referencevalues databases 1132, 1142, 1156, 1143 and/or any portion or componentthereof can comprise separate databases. In another example, the plaquerisk/treatment database 1140, 1162 and one or more of the referencevalues databases 1132, 1142, 1156, 1143 In another example, all or someof the reference values databases 1132, 1142, 1156, 1143 and/or anyportion or component thereof can be part of the same database. In someembodiments, the reference values stored on one or more such referencevalues databases can be curated, triaged, filtered, or selected based onage, gender, race, pre-existing medical condition, and/or any otherpatient/subject characteristic to match the subject for improvedaccuracy.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1122-1160 in FIG. 11D, forexample for one or more other regions of plaque and/or other subjectsand/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease trackingand/or personalized treatment for a subject.

FIG. 11E is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for cardiovascular risk and/or diseasestate assessment using image-based analyses of non-calcified plaque. Thesame reference numbers in FIGS. 11A-E represent similar features and caninclude any of the features described in reference to either figure.

As illustrated in FIG. 11E, in some embodiments, the system can beconfigured to generate a non-calcium score and use the non-calcium scorealone to determine a risk or state of cardiovascular disease or healthof a subject and/or propose a treatment. However, in contrast to someembodiments described herein, in some embodiments as illustrated in FIG.11E, the system can be configured to generate a non-calcium scoredirectly from the determined one or more non-calcified plaque parametersand/or relational parameters, without referencing reference valuesand/or normal values. Rather, in some embodiments, the system can beconfigured to reference values of a non-calcium score after generatingthe same based on image processing techniques.

More specifically, as illustrated in FIG. 11E, in some embodiments, thesystem can be configured to generate a non-calcium score at block 1140and analyze the same based on comparison to one or more reference valuesat block 1144, which may include normal values. The one or morereference values of non-calcium scores can be stored in a referencevalues database 1143 as described herein.

In some embodiments, based on a non-calcium score generated and/orderived without referencing reference values, the system can beconfigured to determine the risk and/or state of cardiovascular diseaseor state of the subject at block 1166. Further, in some embodiments,based on a non-calcium score generated and/or derived withoutreferencing reference values, the system, at block 1166, can beconfigured to determine a proposed treatment for the subject. Thetreatment can include, for example, medical treatment such as statins,interventional treatment such as stent implantation, and/or lifestyletreatment such as exercise or diet. In some embodiments, in determiningthe risk or state of cardiovascular disease or health and/or treatment,the system can access a plaque risk/treatment database 1168, which canbe locally accessible by the system and/or can be located remotely andaccessible through a network connection. In some embodiments, the plaquerisk/treatment database 1168 can include reference points or data thatrelate one or more treatment to cardiovascular disease risk or statedetermined based on non-calcium scores derived without reference toreference values and/or normal values.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1122-1166, for example for oneor more other regions of plaque and/or other subjects and/or for thesame subject at a different time. As such, in some embodiments, thesystem can provide for longitudinal disease tracking and/or personalizedtreatment for a subject.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 11F. The example computer system 1170 is incommunication with one or more computing systems 1688 and/or one or moredata sources 1190 via one or more networks 1186. While FIG. 16Fillustrates an embodiment of a computing system 1170, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1170 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1170 can comprise a Plaque Analysis and/or RiskAssessment Module 1182 that carries out the functions, methods, acts,and/or processes described herein. The Plaque Analysis and/or RiskAssessment Module 1182 executed on the computer system 1170 by a centralprocessing unit 1178 discussed further below. Other features of thecomputer system 1170 can be similar to corresponding features of thecomputer system of FIG. 9G, described above.

Certain Examples of Types of Plaque Composition and Non-Calcium Score

The following are non-limiting examples of certain embodiments ofsystems and methods for types of plaque composition and non-calciumscore. Other embodiments may include one or more other features, ordifferent features, that are discussed herein.

The following are non-limiting examples of certain embodiments ofsystems and methods for types of plaque composition and non-calciumscore. Other embodiments may include one or more other features, ordifferent features, that are discussed herein.

Embodiment 1: A computer-implemented method of assessing a state ofcardiovascular disease of a subject based on multi-dimensionalinformation derived from non-invasive medical image analysis, the methodcomprising: accessing, by a computer system, a medical image of asubject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries, wherein the one or morearteries comprise one or more regions of plaque; determining, by thecomputer system, one or more vascular parameters associated with thesubject by analyzing the one or more arteries identified from themedical image, wherein the one or more vascular parameters comprise oneor more of vascular volume, diameter, area, cross-sectional area,surface area, length, location, or remodeling; identifying, by thecomputer system, the one or more regions of plaque on the medical image;characterizing, by the computer system, one or more of the one or moreidentified regions of plaque as non-calcified plaque, wherein the one ormore identified regions of plaque is characterized as non-calcifiedplaque when radiodensity values of one or more pixels within the one ormore identified regions of plaque is below a predetermined threshold;determining, by the computer system, one or more non-calcified plaqueparameters for the one or more characterized non-calcified plaque, theone or more non-calcified plaque parameters comprising one or more ofplaque density, radiodensity, location, volume, surface area, geometry,heterogeneity, diffusivity, mass or ratio between volume and surfacearea; analyzing, by the computer system, one or more of the one or morevascular parameters or one or more non-calcified plaque parameters bycomparison to a dataset of values, the values comprising one or more ofthe one or more vascular parameters or one or more non-calcified plaqueparameters derived from a population with varying states ofcardiovascular disease; generating, by the computer system, anon-calcium score for the subject, the non-calcium score generated basedat least in part by analyzing one or more of the one or more vascularparameters or one or more non-calcified plaque parameters by comparisonto a dataset of values; and determining, by the computer system, anassessment of the state of cardiovascular disease of the subject basedat least in part on the generated non-calcium score, wherein thecomputer system comprises a computer processor and an electronic storagemedium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinone or more of the one or more identified regions of plaque is furthercharacterized as non-calcified plaque when distribution of radiodensityvalues of one or more pixels within the one or more identified regionsof plaque is above a predetermined threshold.

Embodiment 3: The computer-implemented method of Embodiment 1, furthercomprising characterizing, by the computer system, one or more of theone or more identified regions of plaque as calcified plaque, whereinthe one or more identified regions of plaque is characterized ascalcified plaque when radiodensity values of one or more pixels withinthe one or more identified regions of plaque is above a predeterminedthreshold.

Embodiment 4: The computer-implemented method of Embodiment 3, furthercomprising determining, by the computer system, one or more calcifiedplaque parameters for the one or more characterized calcified plaque,the one or more calcified plaque parameters comprising one or more ofplaque density, radiodensity, location, volume, surface area, geometry,heterogeneity, diffusivity, mass or ratio between volume and surfacearea.

Embodiment 5: The computer-implemented method of Embodiment 4, furthercomprising analyzing, by the computer system, one or more of the one ormore calcified plaque parameters by comparison to a dataset of values ofthe one or more calcified plaque parameters derived from a populationwith varying states of cardiovascular disease.

Embodiment 6: The computer-implemented method of Embodiment 5, furthercomprising generating, by the computer system, a calcium score for thesubject, the calcium score generated based at least in part by analyzingone or more of the one or more calcified plaque parameters by comparisonto the dataset of values of the one or more calcified plaque parameters.

Embodiment 7: The computer-implemented method of Embodiment 6, furthercomprising generating, by the computer system, a weighted measure of thenon-calcium score and the calcium score.

Embodiment 8: The computer-implemented method of Embodiment 7, whereinthe weighted measure of the non-calcium score and the calcium score isgenerated by weighting the non-calcium score between 0 and 1 and byweighting the calcium score between 0 and 1.

Embodiment 9: The computer-implemented method of Embodiment 1, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), magnetic resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).

Embodiment 10: The computer-implemented method of Embodiment 7, whereinthe weighted measure of the non-calcium score and the calcium score isgenerated by weighting the non-calcium score 1 and the calcium score 0.

Embodiment 11: The computer-implemented method of Embodiment 7, whereinthe weighted measure of the non-calcium score and the calcium score isgenerated by weighting the non-calcium score 0 and the calcium score 1.

Embodiment 12: The computer-implemented method of Embodiment 7, whereinthe assessment of the state of cardiovascular disease of the subject isdetermined based at least in part on the generated weighted measure ofthe non-calcium score and the calcium score.

Embodiment 13: The computer-implemented method of Embodiment 8, whereinthe assessment of the state of cardiovascular disease of the subject isdetermined by comparing the generated weighted measure of thenon-calcium score and the calcium score against a dataset of values ofweighted measures of non-calcium score and calcium score for apopulation with varying states of cardiovascular disease.

Embodiment 14: The computer-implemented method of Embodiment 1, furthercomprising generating, by the computer system, a treatment forcardiovascular disease for the subject based at least in part on thedetermined assessment of the state of cardiovascular disease.

Embodiment 15: The computer-implemented method of Embodiment 14, whereinthe treatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.

Embodiment 16: The computer-implemented method of Embodiment 14, furthercomprising tracking, by the computer system, efficacy of the treatmentby determining assessment of the state of cardiovascular disease of thesubject at a later point in time after treatment.

Embodiment 17: The computer-implemented method of Embodiment 1, furthercomprising: determining, by the computer system, one or more relationalparameters, the relational parameters comprising one or more of a ratioof surface area of plaque to surface of vessel, ratio of volume ofplaque to volume of vessel, or a ratio of thickness of plaque tothickness of vessel; and analyzing, by the computer system, one or moreof the one or more relational parameters by comparison to a dataset ofvalues, the values comprising one or more of the one or more relationalparameters derived from a population with varying states ofcardiovascular disease, wherein the non-calcium score for the subject isfurther generated based at least in part on the analyzed one or morerelational parameters.

Embodiment 18: A system for assessing a state of cardiovascular diseaseof a subject based on multi-dimensional information derived fromnon-invasive medical image analysis, the system comprising: one or morecomputer readable storage devices configured to store a plurality ofcomputer executable instructions; and one or more hardware computerprocessors in communication with the one or more computer readablestorage devices and configured to execute the plurality of computerexecutable instructions in order to cause the system to: access amedical image of a subject, wherein the medical image of the subject isobtained non-invasively; analyze the medical image of the subject toidentify one or more arteries, wherein the one or more arteries compriseone or more regions of plaque; identify the one or more regions ofplaque on the medical image; characterize one or more of the one or moreidentified regions of plaque as non-calcified plaque, wherein the one ormore identified regions of plaque is characterized as non-calcifiedplaque when radiodensity values of one or more pixels within the one ormore identified regions of plaque is below a predetermined threshold;determine one or more non-calcified plaque parameters for the one ormore characterized non-calcified plaque, the one or more non-calcifiedplaque parameters comprising one or more of plaque density,radiodensity, location, volume, surface area, geometry, heterogeneity,diffusivity, or ratio between volume and surface area; analyze one ormore of the one or more non-calcified plaque parameters by comparison toa dataset of values, the values comprising one or more of the one ormore non-calcified plaque parameters derived from a population withvarying states of cardiovascular disease; generate a non-calcium scorefor the subject, the non-calcium score generated based at least in partby analyzing one or more of the one or more non-calcified plaqueparameters by comparison to a dataset of values; and determine anassessment of the state of cardiovascular disease of the subject basedat least in part on the generated non-calcium score.

Embodiment 19: The system of Embodiment 18, wherein the system isfurther caused to: characterize one or more of the one or moreidentified regions of plaque as calcified plaque; determine one or morecalcified plaque parameters for the one or more characterized calcifiedplaque, the one or more calcified plaque parameters comprising one ormore of plaque density, radiodensity, location, volume, surface area,geometry, heterogeneity, diffusivity, mass or ratio between volume andsurface area; analyze one or more of the one or more calcified plaqueparameters by comparison to a dataset of values of the one or morecalcified plaque parameters derived from a population with varyingstates of cardiovascular disease; and generate a calcium score for thesubject, the calcium score generated based at least in part by analyzingone or more of the one or more calcified plaque parameters by comparisonto the dataset of values of the one or more calcified plaque parameters.

Embodiment 20: The system of Embodiment 19, wherein the system isfurther caused to generate a weighted measure of the non-calcium scoreand the calcium score, wherein the assessment of the state ofcardiovascular disease of the subject is determined based at least inpart on the generated weighted measure of the non-calcium score and thecalcium score.

Embodiment 21: The system of Embodiment 20, wherein the weighted measureof the non-calcium score and the calcium score is generated by weightingthe non-calcium score between 0 and 1 and by weighting the calcium scorebetween 0 and 1.

Modified and/or Normalized Percent Atheroma Volume (PAV) Introduction

As discussed herein, disclosed herein are systems, methods, and devicesfor cardiovascular risk and/or disease state assessment usingimage-based analyses. In particular, in some embodiments, the systems,devices, and methods are related to cardiovascular risk and/or diseaseand/or state assessment using modified and/or normalized imageanalysis-based plaque parameters. In some embodiments, assessment ofcardiovascular risk and/or disease and/or state generated using thesystems, methods, and devices herein can be utilized to diagnose and/orgenerate a proposed treatment for a patient.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to analyze one or more non-invasivelyobtained medical images of a subject, such as a CT image, to determineone or more plaque parameters and/or relational plaque parameters, suchas for example percent atheroma volume (PAV). In some embodiments, thesystems, devices, and methods described herein can be configured toutilize one or more modified relational plaque parameters, such as amodified version of PAV. PAV can refer to the proportion of total vesselwall volume or total vessel volume occupied by atherosclerotic plaque.As such, analyzing coronary PAV can provide an indication of risk ofcardiovascular disease or a major adverse cardiovascular event (MACE),such as a myocardial infarction or heart attack. In some embodiments,one or more medical images obtained from a coronary computed tomographyangiography (CCTA) can be used as a non-invasive measure to assess PAV.However, in certain scanners and/or scan parameters, the quality of animage obtained from a CT scan or CCTA can be less than perfect. Forexample, in some embodiments, small vessels below a certain size can bedifficult to analyze, thereby potentially resulting in less thanaccurate analysis of plaque or PAV within such vessel.

In order to address such technical shortcomings, some embodiments of thesystems, devices, and methods described herein are configured to utilizea modified PAV in analyzing a CT or CCTA image. In particular, in someembodiments, the system can be configured to analyze a CT or CCTA imageto identify one or more vessels above a certain threshold level andanalyze such vessels to determine PAV. In some embodiments, suchmodified PAV can be further normalized against a physical property ofthe subject, such as for example body mass, left ventricular (LV) mass,heart mass, and/or the like. In some embodiments, such modified and/ornormalized PAV can be compared to a reference database of known modifiedand/or normalized PAV values, wherein such modified and/or normalizedPAV values are obtained by applying the same vessel threshold to medicalimages obtained from a population with varying levels of plaque andnormalized against the same physical property of each subject.

As such, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to utilize a modified and/ornormalized PAV in assessing the risk of coronary artery disease (CAD)and/or MACE for a subject and/or determine a proposed treatment. Byutilizing such modified and/or normalized PAV, it can be possible toaddress image quality issues arising from CT or CCTA scans, as amodified and/or normalized database of PAV values developed using thesame vessel threshold and/or normalized against the same physicalproperty can be used as a reference database.

In some embodiments, the systems, devices, and methods can be configuredto apply such vessel threshold prior to analyzing an image for anyparameter, such as for example any plaque parameter, vessel parameter,and/or relational parameter between the two, including but not limitedto PAV. In some embodiments, any such modified parameter after applyinga vessel threshold can be normalized, for example against a physicalproperty of the subject. For example, in some embodiments, the vesselthreshold can comprise a diameter of about 2.0 mm, such that the systemis configured to ignore any vessels with a diameter below 2.0 mm andanalyze only vessel areas with a diameter above 2.0 mm to determine anysuch parameters. In some embodiments, parameters derived from vesselareas above the vessel threshold can then be normalized against someproperty of the subject and analyzed by comparison to a database ofknown parameter values obtained from vessel areas above the same vesselthreshold from a population with varying degrees of plaque and/ordisease, thereby normalizing the analysis to be independent of the imagequality.

In some embodiments, the vessel threshold can be based on volume,diameter, surface area, radius, width, and/or any other variable orparameter related to the vessel. In some embodiments, the vesselthreshold can comprise a vessel diameter of about mm, about 9.0 mm,about 8.0 mm, about 7.0 mm, about 6.0 mm, about 5.0 mm, about 4.5 mm,about 4.0 mm, about 3.9 mm, about 3.8 mm, about 3.7 mm, about 3.6 mm,about 3.5 mm, about 3.4 mm, about 3.3 mm, about 3.2 mm, about 3.1 mm,about 3.0 mm, about 2.9 mm, about 2.8 mm, about 2.7 mm, about 2.6 mm,about 2.5 mm, about 2.4 mm, about 2.3 mm, about 2.2 mm, about 2.1 mm,about 2.0 mm, about 1.9 mm, about 1.8 mm, about 1.7 mm, about 1.6 mm,about 1.5 mm, about 1.4 mm, about 1.3 mm, about 1.2 mm, about 1.1 mm,about 1.0 mm, about mm, about 0.8 mm, about 0.7 mm, about 0.6 mm, about0.5 mm, about 0.4 mm, about 0.5 mm, about 0.4 mm, about 0.3 mm, about0.2 mm, about 0.1 mm, and/or within a range defined by two of theaforementioned values.

In some embodiments, the system can be configured to utilize a differentvessel threshold for different vessels on an image. For example, in somecases, an image can comprise vessels with varying degrees of quality,such that one vessel has a higher image quality than another vesselwithin the same image due to motion artifact or other reasons. In someembodiments, the system can be configured to apply a higher vesselthreshold to a vessel with lower image quality than another vessel witha higher image quality. Similarly, in some embodiments, the system canbe configured to apply a lower vessel threshold to a vessel with ahigher image quality than another vessel with a lower image quality. Insome embodiments, one or more plaque, vessel, and/or relationalparameters can be derived from an image after applying different vesselthresholds to different vessels. For example, in some instances, a PAVfor one vessel can be derived after removing all vessel areas with adiameter smaller than about 2.0 mm, whereas a PAV for another vesselwithin the same image can be derived after removing all vessel areaswith a diameter smaller than about 1.0 mm.

In some embodiments, the reference database can comprise PAV or otherplaque, vessel, and/or relational parameters derived from images afterapplying varying vessel thresholds to the same and/or different vessels.For example, in some embodiments, the reference database can include PAVderived from one vessel after removing all vessel areas with a diametersmaller than about 2.0 mm, about 1.0 mm, and/or any other vesselthreshold as described herein. Then, in some embodiments, if acorresponding vessel in the subject image at hand is applied a vesselthreshold of 1.0 mm, then that vessel segment or a parameter derivedtherefrom can be compared to corresponding vessel segments or parametersderived therefrom in the database after applying a vessel threshold of1.0 mm (with or without normalizing to a physical property of thesubject). Similarly, for another vessel in the subject image or aparameter derived therefrom can be compared to corresponding vesselsegments or parameters derived therefrom in the database after applyinga vessel threshold of 2.0 mm (with or without normalizing to a physicalproperty of the subject). In other words, different vessel thresholdscan be applied to different vessels, and the resulting vessel segmentscan be normalized and/or compared to vessel segments with differentvessel thresholds applied to them in the reference database. As such, insome embodiments, the reference database can include modified and/ornormalized parameters for different vessels. As such, in someembodiments, the system can be configured to compare each vesselsegment, normalized or not, by comparison to the same vessel segment ofdifferent subjects with the same vessel threshold being applied. Thus,in some embodiments, the systems, methods, and devices can be configuredto provide dynamic normalization of vessels for improved accuracy and/oranalysis independent of image quality.

In some embodiments, the medical image is obtained using an imagingtechnique comprising one or more of computed tomography (CT), x-ray,ultrasound, echocardiography, intravascular ultrasound (IVUS), magneticresonance (MR) imaging, optical coherence tomography (OCT), nuclearmedicine imaging, positron-emission tomography (PET), single photonemission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS). For example, in some embodiments, images for thereference database can be derived from one or more or a combination ofsuch imaging modalities for varying degrees of image quality.

In some embodiments, after applying a vessel threshold, one or morevessel segments can be analyzed to determine one or more plaqueparameters, vessel parameters, and/or relational plaque parameters. Forexample, the plaque parameters can include absolute plaque density,relative plaque density, composition, calcification, radiodensity,location, volume, surface area, geometry, heterogeneity, diffusivity,and/or ratio between volume and surface area. The vessel parameters caninclude vessel volume, diameter, area, cross-sectional area, surfacearea, length, location, and/or remodeling. The relational plaqueparameters can include a ratio or other comparison between one or moreplaque parameters and one or more vessel parameters, such as for examplePAV, PAV on a vessel-by-vessel basis, PAV on a segment-by-segment basis,and/or PAV for the whole heart, ratio of surface area of plaque tosurface of vessel or lumen, ratio of volume of plaque to volume ofvessel or lumen, and/or the like.

In some embodiments, the system can be configured to assess the riskand/or state of cardiovascular disease or health based on the modifiedand/or normalized parameter(s) after applying a vessel threshold(s). Insome embodiments, the system can be configured to determine or generatea proposed treatment for the subject based on the assessed risk and/orstate of cardiovascular disease or health. For example, the proposedtreatment can include one or more of medical therapy (such as statins),interventional therapy (such as stent implantation), and/or lifestyletherapy (such as diet or exercise). In some embodiments, the system canbe configured to track the efficacy of a treatment by tracking changesin the modified and/or normalized parameter(s), for example compared toprevious value(s) for the same subject and/or change relative to areference values database comprising one or more reference values, suchas for example normal values.

As such, in some embodiments, the systems, devices, and methodsdescribed herein provide an improved quantitative and/or image-basedsolution for generating and/or tracking cardiovascular disease or healthby modifying and/or normalizing one or more plaque, vessel, and/orrelational plaque parameters.

As an illustrative example, FIG. 12A provides a schematic example of oneor more regions of plaque that can be analyzed using image analysisprocesses by one or more embodiments of the systems, methods, anddevices herein for assessment of cardiovascular risk, disease, and/orstate.

As illustrated in FIG. 12A, in some embodiments, the systems, methods,and devices described herein can be configured to analyze one or morevessels 1200 on a medical image. The one or more vessels 1200 can taperalong a longitudinal axis such that the diameter of the vessel generallydecreases. As described herein, in some embodiments, the quality of themedical image can be such that certain vessel features, such as plaque1212, in a small or narrow vessel segment can appear less than accuratefor analysis purposes. In contrast, certain vessel features, such assome other regions of plaque 1202, 1204, within the same vessel 1200 canhave sufficient image quality for analysis purposes. For example, insome embodiments, a region of plaque 1202 appearing in a sufficientlywide vessel segment can be further analyzed by the system to identifyand/or determine one or more regions and/or types and/or compositions ofplaque 1206, 1208, 1210 within the region of plaque 1202. In someembodiments, the one or more regions of plaque 1204 can comprise asingle type or composition of plaque. In some embodiments, a region ofplaque 1202, 1204, 1206, 1208, 1210 can comprise one or more differenttypes or compositions of plaque. For example, in some embodiments, thesystem can be configured to analyze, characterize, and/or classifyplaque into one of three types: low-density non-calcified plaque,non-calcified plaque, and calcified plaque. In some embodiments, thesystem can be configured to analyze, characterize, and/or classifyplaque into one of two types: non-calcified plaque and calcified plaque.In some embodiments, the system can be configured to analyze,characterize, and/or classify plaque into one of any number of differenttypes, such as for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,and/or 30 different types of classifications of plaque. In someembodiments, the system can be configured to analyze, characterize,and/or classify plaque into a number of different types within a rangedefined by two of the aforementioned values.

In some embodiments, due to the fact that certain vessel features, suchas a region of plaque 1212, within a narrow vessel segment comprises lowimage quality for analysis, the system can be configured to ignore orremove such features. More specifically, as described herein, in someembodiments, the system can be configured to ignore vessel segmentsbelow a certain vessel threshold 1214 and/or features therein fromfurther analysis. As such, in the illustrated example, in someembodiments, the system can be configured to only analyze some of theregions of plaque 1202, 1204 that are above a vessel threshold 1214 andnot others 1212 that are below the vessel threshold 1214 within a singlevessel 1200. In some embodiments, the system can be further configuredto analyze only those regions of plaque 1202, 1204 above the vesselthreshold 1214 in determining one or more plaque, vessel, and/orrelational plaque parameters, such as PAV. In some embodiments, suchmodified PAV or other parameter can be normalized, for example againstthe body mass and/or LV mass and/or heart mass of the subject, and/orcompared to a normal values and/or reference values database comprisingmodified and/or normalized PAV or other parameter values. Based on suchanalysis, in some embodiments, the system can be configured to determinecoronary artery disease (CAD) risk assessment and/or proposed treatmentfor a subject.

Cardiovascular Risk and/or Disease State Assessment Using Modifiedand/or Normalized Image Analysis-Based Plaque Parameters

As described herein, some embodiments of systems, devices, and methodsdescribed herein are configured to derive one or more modified plaqueparameters, for example by setting a vessel threshold for analysis, froma medical image and use the same for risk assessment and/or treatmentassessment for CAD. In some embodiments, such modified plaque parameterscan be normalized for comparison and/or analysis against a referencedatabase in determining the risk and/or treatment assessment. FIG. 12Bis a flowchart illustrating example embodiments of systems, devices, andmethods for cardiovascular risk and/or disease state assessment usingmodified and/or normalized image analysis-based plaque parameters.

As illustrated in FIG. 12B, in some embodiments, the system can beconfigured to access a medical image at block 1216. In some embodiments,the medical image can include one or more arteries, such as coronary,carotid, and/or other arteries of a subject. In some embodiments, themedical image can be stored in a medical image database 1218. In someembodiments, the medical image database 1218 can be locally accessibleby the system and/or can be located remotely and accessible through anetwork connection. The medical image can comprise an image obtain usingone or more modalities such as for example, CT, Dual-Energy ComputedTomography (DECT), Spectral CT, photon-counting CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), Magnetic Resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).In some embodiments, the medical image comprises one or more of acontrast-enhanced CT image, non-contrast CT image, MR image, and/or animage obtained using any of the modalities described above.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 1220, thesystem can be configured to analyze the quality of the medical imageand/or scan parameters. In some embodiments, the system can beconfigured to analyze the quality of the image as a whole and/or on avessel-by-vessel or segment-by-segment basis.

In some embodiments, at block 1222, the system can be configured todetermine and/or apply a vessel threshold value. As discussed herein, insome embodiments, the vessel threshold value can be determined and/orapplied on the image as a whole and/or on a vessel-by-vessel orsegment-by-segment basis. The vessel threshold can be predeterminedand/or be dynamically determined, automatically, semi-automatically,and/or manually.

In some embodiments, the system can be configured to utilize one or moreAI and/or ML algorithms to automatically and/or dynamically determineand/or apply a vessel threshold value. For example, in some embodiments,the one or more AI and/or ML algorithms can be trained using aConvolutional Neural Network (CNN) on a set of medical images on whichvessel thresholds have been identified, for example depending on theimage quality, thereby allowing the AI and/or ML algorithm automaticallydetermine and/or apply a vessel threshold(s) directly based on a medicalimage.

In some embodiments, the vessel threshold can be based on volume,diameter, surface area, radius, width, and/or any other variable orparameter related to the vessel. In some embodiments, the vesselthreshold can comprise a vessel diameter of about mm, about 9.0 mm,about 8.0 mm, about 7.0 mm, about 6.0 mm, about 5.0 mm, about 4.5 mm,about 4.0 mm, about 3.9 mm, about 3.8 mm, about 3.7 mm, about 3.6 mm,about 3.5 mm, about 3.4 mm, about 3.3 mm, about 3.2 mm, about 3.1 mm,about 3.0 mm, about 2.9 mm, about 2.8 mm, about 2.7 mm, about 2.6 mm,about 2.5 mm, about 2.4 mm, about 2.3 mm, about 2.2 mm, about 2.1 mm,about 2.0 mm, about 1.9 mm, about 1.8 mm, about 1.7 mm, about 1.6 mm,about 1.5 mm, about 1.4 mm, about 1.3 mm, about 1.2 mm, about 1.1 mm,about 1.0 mm, about mm, about 0.8 mm, about 0.7 mm, about 0.6 mm, about0.5 mm, about 0.4 mm, about 0.5 mm, about 0.4 mm, about 0.3 mm, about0.2 mm, about 0.1 mm, and/or within a range defined by two of theaforementioned values.

In some embodiments, at block 1224, the system can be configured toidentify and/or characterize one or more vessels and/or lumen ofinterest, such as of one or more arteries. The one or more arteries caninclude coronary arteries, carotid arteries, aorta, renal artery, lowerextremity artery, upper extremity artery, and/or cerebral artery,amongst others. In some embodiments, the system can be configured toutilize one or more AI and/or ML algorithms to automatically and/ordynamically identify one or more arteries or coronary arteries usingimage processing. For example, in some embodiments, the one or more AIand/or ML algorithms can be trained using a Convolutional Neural Network(CNN) on a set of medical images on which arteries or coronary arterieshave been identified, thereby allowing the AI and/or ML algorithmautomatically identify arteries or coronary arteries directly from amedical image. In some embodiments, the arteries or coronary arteriesare identified by size and/or location.

In some embodiments, the one or more vessels and/or lumen of interestcan be identified based on the vessel threshold(s) being applied. Forexample, in some embodiments, the system can be configured to identifyand/or characterize only those vessel or lumen segments that qualifyand/or are above the vessel threshold, while ignoring other vessel orlumen segments below the vessel threshold. Thus, in some embodiments,the system can be configured to same processing power and increaseprocessing speed by ignoring areas within certain vessels that wouldonly produce sub-par analysis results due to low image quality.

In some embodiments, at block 1226, the system can be configured toidentify and/or determine one or more vascular parameters. In someembodiments, vascular parameters can include one or more of vascularvolume, diameter, area, cross-sectional area, surface area, length,location, and/or remodeling. The system can be configured to utilize oneor more AI and/or ML algorithms to automatically and/or dynamicallyidentify and/or determine one or more vascular parameters using imageprocessing.

In some embodiments, at block 1228, the system can be configured toidentify and/or characterize one or more regions of plaque within themedical image. In some embodiments, the one or more regions of plaquecan be identified based on the vessel threshold(s) being applied. Forexample, in some embodiments, the system can be configured to identifyand/or characterize only those regions of plaque that are within avessel segment that qualify and/or are above the vessel threshold, whileignoring other regions of plaque that are within a vessel or lumensegments below the vessel threshold. Thus, in some embodiments, thesystem can be configured to same processing power and increaseprocessing speed by ignoring areas of plaque within certain vessels thatwould only produce sub-par analysis results due to low image quality.

In some embodiments, the system can be configured to utilize one or moreAI and/or ML algorithms to automatically and/or dynamically identifyand/or characterize one or more regions of plaque using imageprocessing. For example, in some embodiments, the one or more AI and/orML algorithms can be trained using a Convolutional Neural Network (CNN)on a set of medical images on which one or more regions of plaque havebeen identified and/or characterized, thereby allowing the AI and/or MLalgorithm to automatically identify and/or characterize regions ofplaque directly from a medical image.

For example, in some embodiments, the system can be configured toanalyze the absolute density, relative density, radiodensity values,and/or heterogeneity or distribution thereof of one or more pixels on amedical image to identify and/or characterize plaque. More specifically,in some embodiments, the system can be configured to identify aparticular pixel as plaque, non-calcified plaque, low-densitynon-calcified plaque, and/or calcified plaque, and/or any otherclassification or type of plaque. In some embodiments, the system can beconfigured to classify a particular pixel or region of plaque asnon-calcified plaque when the radiodensity value of the pixel is below acertain predetermined threshold, within a predetermined range, and/orcomprises a heterogeneity index or distribution within, under, or abovea particular predetermined range or threshold. In some embodiments, thesystem can be configured to classify a particular pixel or region ofplaque as low-density non-calcified plaque when the radiodensity valueof the pixel is below a certain predetermined threshold, within apredetermined range, and/or comprises a heterogeneity index ordistribution within, under, or above a particular predetermined range orthreshold. In some embodiments, the system can be configured to classifya particular pixel or region of plaque as calcified plaque when theradiodensity value of the pixel is above a certain predeterminedthreshold, within a predetermined range, and/or comprises aheterogeneity index or distribution within, under, or above a particularpredetermined range or threshold.

In some embodiments, at block 1230, the system can be configured todetermine one or more non-calcified plaque parameters and/or one or morerelational plaque parameters. For example, in some embodiments, the oneor more plaque parameters can include absolute plaque density, relativeplaque density, composition, calcification, radiodensity, location,volume, surface area, geometry, heterogeneity, diffusivity, and/or ratiobetween volume and surface area. The relational plaque parameters caninclude a ratio or other comparison between one or more plaqueparameters and one or more vessel parameters, such as for example PAV,PAV on a vessel-by-vessel basis, PAV on a segment-by-segment basis,and/or PAV for the whole heart, ratio of surface area of plaque tosurface of vessel or lumen, ratio of volume of plaque to volume ofvessel or lumen, and/or the like.

In some embodiments, the system can be configured to utilize one or moreAI and/or ML algorithms to automatically and/or dynamically determineone or more plaque parameters and/or relational plaque parameters usingimage processing. For example, in some embodiments, the one or more AIand/or ML algorithms can be trained using a Convolutional Neural Network(CNN) on a set of medical images on which one or more plaque parametersand/or relational plaque parameters have been determined, therebyallowing the AI and/or ML algorithm to automatically determine one ormore plaque parameters and/or relational plaque parameters directly froma medical image.

In some embodiments, at block 1232, the system can be configured tonormalize one or more of the one or more modified plaque parameters,vascular parameters, and/or relational plaque parameters. For example,in some embodiments, the system can be configured to normalize one ormore parameters by comparing to a physical property of the subject, suchas for example heart mass, LV mass, total vessel volume, age, gender,and/or a combination or weighted measure of the foregoing.

In some embodiments, at block 1234, the system can be configured togenerate a weighted measure of the normalized and/or modified one ormore parameters. For example, in some embodiments, the system can beconfigured to assign a weight between 0 and 1 to one or more normalizedplaque parameters, vascular parameters, and/or relational plaqueparameters. In some embodiments, the system can be configured to assigna weight between 0 and 1, such as for example 0, 0.05, 0.1, 0.15, 0.2,0.3, 0.4, 0.5. 0.6, 0.7, 0.8, 0.9, 0.99, and 1. In some embodiments, thesystem can be configured weight one or more parameters 0, therebyignoring its effect. In some embodiments, the system can be configuredto weight one or more parameters 1, thereby focusing exclusively on suchparameter. In some embodiments, the weighted measure can also includeone or more other factors or features, such as for example, age, weight,gender, plaque volume, plaque composition, vascular remodeling,high-risk plaque, lumen volume, plaque location (proximal v. middle v.distal), plaque location (myocardial v. pericardial facing), plaquelocation (at bifurcation or trifurcation v. not at bifurcation ortrifurcation), plaque location (in main vessel v. branch vessel),stenosis severity, percentage coronary blood volume, percentagefractional myocardial mass, percentile for age and/or gender, constantor other correction factor to allow for control of within-person,within-vessel, inter-plaque, plaque-myocardial relationships, and/or thelike.

In some embodiments, at block 1236, the system can be configured analyzethe generated weighted measure of the normalized and/or modified one ormore of the one or more plaque parameters, vascular parameters, and/orrelational plaque parameters. For example, in some embodiments, thesystem can be configured to analyze the weighted measure against one ormore reference values, such as normal values. More specifically, in someembodiments, the system can be configured to access a reference valuesdatabase 1238 that includes a weighted measure of one or more referencevalues of one or more normalized and/or modified plaque parameters,vascular parameters, and/or relational plaque parameters. The one ormore reference values can be derived from other subjects with varyingstates or risks of cardiovascular disease, including for example normalvalues.

In some embodiments, the one or more reference values can be obtainedfrom one or more medical images using the same or similar imagingmodalities as the medical image accessed at block 1216. In someembodiments, the one or more reference values can be obtained fromanalyzing one or more medical image to derive one or more modifiedplaque, relational plaque, and/or vascular parameters after applying oneor more vessel thresholds, including for example the same vesselthreshold applied at block 1222. In some embodiments, the one or morereference values can include one or more parameters obtained from amedical image after applying varying different vessel thresholds to thesame image and/or vessel. In some embodiments, the one or more referencevalues can be obtained after normalizing to a physical property,including for example the same physical property used to normalize theone or more parameters in block 1232. In some embodiments, the one ormore reference values can include one or more parameters obtained from amedical image after normalizing against a number of varying differentphysical properties, such as body mass, heart mass, LV mass, and/or thelike, to the same image and/or parameter. In some embodiments, aweighted measure of the one or more reference values can be stored on areference values database 1238, which can be locally accessible by thesystem and/or can be located remotely and accessible through a networkconnection.

In some embodiments, based on such analysis and/or comparison, thesystem, at block 1240, can be configured to determine a risk or state ofcardiovascular disease or health of the subject. Further, in someembodiments, based on such analysis and/or comparison, the system, atblock 1240, can be configured to determine a proposed treatment for thesubject. The treatment can include, for example, medical treatment suchas statins, interventional treatment such as stent implantation, and/orlifestyle treatment such as exercise or diet. In some embodiments, indetermining the risk or state of cardiovascular disease or health and/ortreatment, the system can access a plaque risk/treatment database 1242,which can be locally accessible by the system and/or can be locatedremotely and accessible through a network connection. In someembodiments, the plaque risk/treatment database 1242 can includereference points or data that relate one or more treatment tocardiovascular disease risk or state determined based on one or morenormalized and/or modified one or more plaque, vascular, and/orrelational plaque parameters.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1216-1240, for example for oneor more other regions of plaque and/or other subjects and/or for thesame subject at a different time. As such, in some embodiments, thesystem can provide for longitudinal disease tracking and/or personalizedtreatment for a subject.

FIG. 12C is also a flowchart illustrating example embodiments ofsystems, devices, and methods for cardiovascular risk and/or diseasestate assessment using modified and/or normalized image analysis-basedplaque parameters. The same reference numbers in FIGS. 12B and 12Crepresent similar features and can include any of the features describedin reference to either figure.

As illustrated in FIG. 12C, in some embodiments, the system can beconfigured to analyze one or more normalized plaque, vascular, and/orrelational plaque parameters at block 1244 directly without generating aweighted measure of the same. More specifically, in some embodiments,the system can be configured to access a reference values database 1248that includes one or more reference values of one or more normalizedand/or modified plaque parameters, vascular parameters, and/orrelational plaque parameters. The one or more reference values can bederived from other subjects with varying states or risks ofcardiovascular disease, including for example normal values.

In some embodiments, the one or more reference values can be obtainedfrom one or more medical images using the same or similar imagingmodalities as the medical image accessed at block 1216. In someembodiments, the one or more reference values can be obtained fromanalyzing one or more medical image to derive one or more modifiedplaque, relational plaque, and/or vascular parameters after applying oneor more vessel thresholds, including for example the same vesselthreshold applied at block 1222. In some embodiments, the one or morereference values can include one or more parameters obtained from amedical image after applying varying different vessel thresholds to thesame image and/or vessel. In some embodiments, the one or more referencevalues can be obtained after normalizing to a physical property,including for example the same physical property used to normalize theone or more parameters in block 1232. In some embodiments, the one ormore reference values can include one or more parameters obtained from amedical image after normalizing against a number of varying differentphysical properties, such as body mass, heart mass, LV mass, and/or thelike, to the same image and/or parameter. In some embodiments, the oneor more reference values can be stored on a reference values database1248, which can be locally accessible by the system and/or can belocated remotely and accessible through a network connection.

In some embodiments, at block 1246, the system can be configured togenerate a weighted measure of the referenced and/or normalized and/ormodified one or more plaque, vascular, and/or relational plaqueparameters. For example, in some embodiments, the system can beconfigured to assign a weight between 0 and 1 to one or more referenced,normalized, and/or modified plaque parameters, vascular parameters,and/or relational plaque parameters. In some embodiments, the system canbe configured to assign a weight between 0 and 1, such as for example 0,0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5. 0.6, 0.7, 0.8, 0.9, 0.99, and 1. Insome embodiments, the system can be configured weight one or moreparameters 0, thereby ignoring its effect. In some embodiments, thesystem can be configured to weight one or more parameters 1, therebyfocusing exclusively on such parameter. In some embodiments, theweighted measure can also include one or more other factors or features,such as for example, age, weight, gender, plaque volume, plaquecomposition, vascular remodeling, high-risk plaque, lumen volume, plaquelocation (proximal v. middle v. distal), plaque location (myocardial v.pericardial facing), plaque location (at bifurcation or trifurcation v.not at bifurcation or trifurcation), plaque location (in main vessel v.branch vessel), stenosis severity, percentage coronary blood volume,percentage fractional myocardial mass, percentile for age and/or gender,constant or other correction factor to allow for control ofwithin-person, within-vessel, inter-plaque, plaque-myocardialrelationships, and/or the like.

In some embodiments, the system, at block 1250, can be configured to usethe generated weighted measure of the referenced and/or normalizedand/or modified one or more plaque, vascular, and/or relational plaqueparameters to determine a risk or state of cardiovascular disease orhealth of the subject. Further, in some embodiments, the system, atblock 1250 can be configured to determine a proposed treatment for thesubject. The treatment can include, for example, medical treatment suchas statins, interventional treatment such as stent implantation, and/orlifestyle treatment such as exercise or diet. In some embodiments, indetermining the risk or state of cardiovascular disease or health and/ortreatment, the system can access a plaque risk/treatment database 1252,which can be locally accessible by the system and/or can be locatedremotely and accessible through a network connection. In someembodiments, the plaque risk/treatment database 1252 can includereference points or data that relate one or more treatment tocardiovascular disease risk or state determined based on one or moreweighted measures of referenced and/or normalized and/or modified one ormore plaque, vascular, and/or relational plaque parameters.

In some embodiments, the reference values databases 1238, 1248 and/orany portion or component thereof can be part of a single database. Insome embodiments, the reference values databases 1238, 1248 and/or anyportion or component thereof can comprise separate databases. In someembodiments, the reference values stored on one or more such referencevalues databases 1238, 1248 can be curated, triaged or selected based onage, gender, pre-existing medical condition, and/or the like to matchthe subject for improved accuracy.

In some embodiments, the plaque risk/treatment databases 1242, 1252and/or any portion or component thereof can be part of a singledatabase. In some embodiments, the plaque risk/treatment databases 1242,1252 and/or any portion or component thereof can comprise separatedatabases. In some embodiments, the plaque risk and/or treatment storedon one or more such plaque risk/treatment databases 1242, 1252 can becurated, triaged or selected based on age, gender, pre-existing medicalcondition, and/or the like to match the subject for improved accuracy.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1216-1250, for example for oneor more other regions of plaque and/or other subjects and/or for thesame subject at a different time. As such, in some embodiments, thesystem can provide for longitudinal disease tracking and/or personalizedtreatment for a subject.

Reference Values Database Based on Modified and/or Normalized ImageAnalysis-Based Plaque Parameters

As described herein, some embodiments of systems, devices, and methodsdescribed herein are configured to utilize one or more reference valuesof modified and/or normalized plaque parameters, vessel parameters,and/or relational plaque parameters in assessing CAD risk and/ordetermining a proposed treatment for a subject. In order to obtain suchreference values and/or build a reference values database, someembodiments can be configured to utilize one or more vessel thresholdsto modify one or more plaque parameters, vessel parameters, and/orrelational plaque parameters. Further, in order to obtain such referencevalues and/or build a reference values database, some embodiments can beconfigured to normalize one or more modified plaque parameters, vesselparameters, and/or relational plaque parameters against a physicalproperty of the subject, such as for example body mass, heart mass, LVmass, total vessel volume, and/or the like.

FIG. 12D is a flowchart illustrating example embodiments of systems,devices, and methods for developing a reference values database forcardiovascular risk and/or disease state assessment using modifiedand/or normalized image analysis-based plaque parameters. The samereference numbers in FIGS. 12D, 12C, 12B represent similar features andcan include any of the features described in reference to either figure.

As illustrated in FIG. 12D, in some embodiments, the system can beconfigured to store one or more normalized and/or modified plaqueparameters, vessel parameters, and/or relational plaque parameters in areference values database 1258. For example, in some embodiments, thesystem can be configured to store one or more such values in thereference values database 1258 after analysis of a subject, therebycontinuously and/or periodically updating the reference database.

More specifically, in some embodiments, at block 1216, the system can beconfigured to access one or more patient information, assessed CAD risk,and/or proposed or actual treatment, for example by accessing a patientdatabase 1218. In some embodiments, the system can be configured tocorrelate and/or map the modified and/or normalized one or more plaqueparameters, vessel parameters, and/or relational plaque parameters toone or more information accessed from the patient database 1218. In someembodiments, the patient database 1218 can be locally accessible by thesystem and/or can be located remotely and accessible through a networkconnection.

In some embodiments, at block 1260, the system can be configured tostore the normalized and/or modified one or more plaque parameters,vessel parameters, and/or relational plaque parameters and/or mapping toone or more patient information. For example, in some embodiments, thesystem can be configured to store one or more of the foregoing on areference values database 1258 that can be locally accessible by thesystem and/or can be located remotely and accessible through a networkconnection.

In some embodiments, the reference values databases 1238, 1248, 1258and/or any portion or component thereof can be part of a singledatabase. In some embodiments, the reference values databases 1238,1248, 1258 and/or any portion or component thereof can comprise separatedatabases. In some embodiments, the reference values stored on one ormore such reference values databases 1238, 1248, 1258 can be curated,triaged or selected based on age, gender, pre-existing medicalcondition, and/or the like to match the subject for improved accuracy.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1216-1260, for example for oneor more other regions of plaque and/or other subjects and/or for thesame subject at a different time. As such, in some embodiments, thesystem can be configured to continuously and/or periodically update thereference values database 1258, thereby continuously improving accuracy.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 12E. The example computer system 1264 is incommunication with one or more computing systems 1282 and/or one or moredata sources 1284 via one or more networks 1280. While FIG. 12Eillustrates an embodiment of a computing system 1264, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1264 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1264 can comprise a Plaque Analysis and/or RiskAssessment Module 1276 that carries out the functions, methods, acts,and/or processes described herein. The Plaque Analysis and/or RiskAssessment Module 1276 executed on the computer system 1264 by a centralprocessing unit 1268 discussed further below. Other features of thecomputer system 1264 can be similar to corresponding features of thecomputer system of FIG. 9G, described above.

Certain Examples of Embodiments of Modified Percent Atheroma Volume(PAV) and/or Normalized Percent Atheroma Volume

The following are non-limiting examples of certain embodiments ofsystems and methods for determining modified and/or normalized percentatheroma volume (PAV). Other embodiments may include one or more otherfeatures, or different features, that are discussed herein.

Embodiment 1: A computer-implemented method of assessing a state ofcardiovascular disease of a subject based on one or more normalizedrelational plaque parameters derived from non-invasive medical imageanalysis, the method comprising: accessing, by a computer system, amedical image of a subject, wherein the medical image of the subject isobtained non-invasively; analyzing, by the computer system, the medicalimage of the subject to identify one or more arteries; applying, by thecomputer system, a vessel threshold to the one or more arteriesidentified from the medical image to determine one or more arterialsections of interest above the vessel threshold; determining, by thecomputer system, one or more vascular parameters associated with thesubject by analyzing the one or more arterial sections of interest,wherein the one or more vascular parameters comprise one or more ofvessel volume, diameter, area, cross-sectional area, surface area,length, location, or remodeling; identifying, by the computer system,one or more regions of plaque in the arterial sections of interest;determining, by the computer system, one or more plaque parametersassociated with the one or more regions of plaque, wherein the one ormore plaque parameters comprise one or more of plaque density,composition, calcification, radiodensity, location, volume, surfacearea, geometry, heterogeneity, diffusivity, or ratio between volume andsurface area; determining, by the computer system, one or morerelational plaque parameters for the subject, the one or more relationalplaque parameters determined by comparing the one or more plaqueparameters to the one or more vascular parameters; normalizing, by thecomputer system, the one or more relational plaque parameters for thesubject by comparison to one or more physical properties of the subject;analyzing, by the computer system, the normalized one or more relationalplaque parameters for the subject by comparison to a dataset of values,the values comprising a plurality of normalized one or more relationalplaque parameters derived from applying the vessel threshold to aplurality of medical images of a population with varying states ofcardiovascular disease; and determining, by the computer system, anassessment of a state of cardiovascular disease of the subject based atleast in part on analysis of the normalized one or more relationalplaque parameters for the subject, wherein the computer system comprisesa computer processor and an electronic storage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinthe vessel threshold comprises about 2.0 mm in diameter.

Embodiment 3: The computer-implemented method of Embodiment 1, whereinthe one or more relational plaque parameters comprises percent atheromavolume (PAV).

Embodiment 4: The computer-implemented method of Embodiment 3, whereinthe PAV comprises total plaque volume over vessel volume of the one ormore arterial sections of interest.

Embodiment 5: The computer-implemented method of Embodiment 3, whereinthe PAV comprises non-calcified plaque volume over vessel volume of theone or more arterial sections of interest.

Embodiment 6: The computer-implemented method of Embodiment 3, whereinthe PAV comprises low-density non-calcified plaque volume over vesselvolume of the one or more arterial sections of interest.

Embodiment 7: The computer-implemented method of Embodiment 1, whereinthe vessel threshold comprises about 2.0 mm in diameter, and wherein theone or more relational plaque parameters comprises percent atheromavolume (PAV).

Embodiment 8: The computer-implemented method of Embodiment 1, whereinthe one or more physical properties of the subject comprises body massof the subject.

Embodiment 9: The computer-implemented method of Embodiment 1, whereinthe one or more physical properties of the subject comprises leftventricular mass of the subject.

Embodiment 10: The computer-implemented method of Embodiment 9, whereinthe left ventricular mass of the subject is determined based at least inpart on the medical image of the subject.

Embodiment 11: The computer-implemented method of Embodiment 1, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), magnetic resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).

Embodiment 12: The computer-implemented method of Embodiment 1, whereinthe vessel threshold varies by vessel.

Embodiment 13: The computer-implemented method of Embodiment 1, whereinthe vessel threshold is determined based at least in part on quality ofthe medical image.

Embodiment 14: The computer-implemented method of Embodiment 1, whereinthe assessment of the state of cardiovascular disease of the subject isfurther determined based at least in part on the one or more vascularparameters or the one or more plaque parameters.

Embodiment 15: The computer-implemented method of Embodiment 1, furthercomprising: generating, by the computer system, a weighted measure ofone or more of the one or more vascular parameters, one or more plaqueparameters, or one or more relational plaque parameters, wherein theassessment of the state of cardiovascular disease of the subject isfurther determined based at least in part on the generated weightedmeasure.

Embodiment 16: The computer-implemented method of Embodiment 1, furthercomprising generating, by the computer system, a treatment forcardiovascular disease for the subject based at least in part on thedetermined assessment of the state of cardiovascular disease.

Embodiment 17: The computer-implemented method of Embodiment 16, whereinthe treatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.

Embodiment 18: The computer-implemented method of Embodiment 16, furthercomprising tracking, by the computer system, efficacy of the treatmentby determining assessment of the state of cardiovascular disease of thesubject at a later point in time after treatment.

Embodiment 19: A system for assessing a state of cardiovascular diseaseof a subject based on one or more normalized relational plaqueparameters derived from non-invasive medical image analysis, the systemcomprising: one or more computer readable storage devices configured tostore a plurality of computer executable instructions; and one or morehardware computer processors in communication with the one or morecomputer readable storage devices and configured to execute theplurality of computer executable instructions in order to cause thesystem to: access a medical image of a subject, wherein the medicalimage of the subject is obtained non-invasively; analyze the medicalimage of the subject to identify one or more arteries; apply a vesselthreshold to the one or more arteries identified from the medical imageto determine one or more arterial sections of interest above the vesselthreshold; determine one or more vascular parameters associated with thesubject by analyzing the one or more arterial sections of interest,wherein the one or more vascular parameters comprise one or more ofvessel volume, diameter, area, cross-sectional area, surface area,length, location, or remodeling; identify one or more regions of plaquein the arterial sections of interest; determine one or more plaqueparameters associated with the one or more regions of plaque, whereinthe one or more plaque parameters comprise one or more of plaquedensity, composition, calcification, radiodensity, location, volume,surface area, geometry, heterogeneity, diffusivity, or ratio betweenvolume and surface area; determine one or more relational plaqueparameters for the subject, the one or more relational plaque parametersdetermined by comparing the one or more plaque parameters to the one ormore vascular parameters; normalize the one or more relational plaqueparameters for the subject by comparison to one or more physicalproperties of the subject; analyze the normalized one or more relationalplaque parameters for the subject by comparison to a dataset of values,the values comprising a plurality of normalized one or more relationalplaque parameters derived from applying the vessel threshold to aplurality of medical images of a population with varying states ofcardiovascular disease; and determine an assessment of a state ofcardiovascular disease of the subject based at least in part on analysisof the normalized one or more relational plaque parameters for thesubject.

Embodiment 20: The system of Embodiment 19, wherein the vessel thresholdcomprises about 2.0 mm in diameter.

Embodiment 21: The system of Embodiment 19, wherein the one or morerelational plaque parameters comprises percent atheroma volume (PAV).

Embodiment 22: The system of Embodiment 21, wherein the PAV comprisestotal plaque volume over vessel volume of the one or more arterialsections of interest.

Embodiment 23: The system of Embodiment 21, wherein the PAV comprisesnon-calcified plaque volume over vessel volume of the one or morearterial sections of interest.

Embodiment 24: The system of Embodiment 21, wherein the PAV compriseslow-density non-calcified plaque volume over vessel volume of the one ormore arterial sections of interest.

Embodiment 25: The system of Embodiment 19, wherein the vessel thresholdcomprises about 2.0 mm in diameter, and wherein the one or morerelational plaque parameters comprises percent atheroma volume (PAV).

Embodiment 26: The system of Embodiment 19, wherein the one or morephysical properties of the subject comprises body mass of the subject.

Embodiment 27: The system of Embodiment 19, wherein the one or morephysical properties of the subject comprises left ventricular mass ofthe subject.

Embodiment 28: The system of Embodiment 27, wherein the left ventricularmass of the subject is determined based at least in part on the medicalimage of the subject.

Embodiment 29: The system of Embodiment 19, wherein the medical image isobtained using an imaging technique comprising one or more of computedtomography (CT), x-ray, ultrasound, echocardiography, intravascularultrasound (IVUS), magnetic resonance (MR) imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 30: The system of Embodiment 19, wherein the vessel thresholdvaries by vessel.

Embodiment 31: The system of Embodiment 19, wherein the vessel thresholdis determined based at least in part on quality of the medical image.

Embodiment 32: The system of Embodiment 19, wherein the assessment ofthe state of cardiovascular disease of the subject is further determinedbased at least in part on the one or more vascular parameters or the oneor more plaque parameters.

Embodiment 33: The system of Embodiment 19, wherein the system isfurther caused to: generate a weighted measure of one or more of the oneor more vascular parameters, one or more plaque parameters, or one ormore relational plaque parameters, wherein the assessment of the stateof cardiovascular disease of the subject is further determined based atleast in part on the generated weighted measure.

Embodiment 34: The system of Embodiment 19, wherein the system isfurther caused to generate a treatment for cardiovascular disease forthe subject based at least in part on the determined assessment of thestate of cardiovascular disease.

Embodiment 35: The system of Embodiment 34, wherein the treatment forcardiovascular disease comprises medical intervention, medicaltreatment, or lifestyle change.

Embodiment 36: The system of Embodiment 34, wherein the system isfurther caused to track efficacy of the treatment by determiningassessment of the state of cardiovascular disease of the subject at alater point in time after treatment.

Immersive Patient-Specific Report Introduction

Disclosed herein are systems, methods, and devices for generation of apatient-specific report on the risk and/or state assessment, diagnosis,and/or treatment of cardiovascular disease, including for examplecoronary artery disease (CAD). In particular, in some embodiments, thesystems, devices, and methods are configured to generate an immersivepatient-specific report on the patient's cardiovascular disease risk,state, diagnosis, and/or treatment. In some embodiments, the systems,devices, and methods are configured to generate an immersivepatient-specific report based at least in part on image-based analysis,for example of one or more plaque and/or vessel parameters. In someembodiments, the systems, devices, and methods are configured to viewthe patient's cardiovascular disease state or risk from a point of viewwithin one or more arteries of the patient. In some embodiments, thesystems, devices, and methods are configured to graphically view and/ortrack actual or hypothetical progression of the patient's cardiovasculardisease state or risk based on actual or proposed treatment from a pointof view within one or more arteries of the patient. In some embodiments,the systems, methods, and devices described herein can allow for virtualconsultations and/or physician visits. For example, such virtualconsultations or physician visits can take place in a virtual setting,such as the Metaverse, using augmented reality (AR) or virtual reality(VR) technology.

With respect to the millions suffering from heart disease, there is aneed to better understand the overall health of the artery vesselswithin a patient beyond just knowing the blood chemistry or content ofthe blood flowing through such artery vessels. For example, in someembodiments of systems, devices, and methods disclosed herein, arterieswith “good” or stable plaque or plaque comprising hardened calcifiedcontent are considered non-life threatening to patients whereas arteriescontaining “bad” or unstable plaque or plaque comprising fatty materialare considered more life threatening because such bad plaque may rupturewithin arteries thereby releasing such fatty material into the arteries.Such a fatty material release in the blood stream can cause inflammationthat may result in a blood clot. A blood clot within an artery canprevent blood from traveling to heart muscle thereby causing a heartattack or other cardiac event. Further, in some instances, it isgenerally more difficult for blood to flow through fatty plaque buildupthan it is for blood to flow through calcified plaque build-up.Therefore, there is a need for better understanding and analysis of thearterial vessel walls of a patient.

Further, while blood tests and drug treatment regimens are helpful inreducing cardiovascular health issues and mitigating againstcardiovascular events (for example, heart attacks), such treatmentmethodologies are not complete or perfect in that such treatments canmisidentify and/or fail to pinpoint or diagnose significantcardiovascular risk areas. For example, the mere analysis of the bloodchemistry of a patient will not likely identify that a patient hasartery vessels having significant amounts of fatty deposit material badplaque buildup along a vessel wall. Similarly, an angiogram, whilehelpful in identifying areas of stenosis or vessel narrowing, may not beable to clearly identify areas of the artery vessel wall where there issignificant buildup of bad plaque. Such areas of buildup of bad plaquewithin an artery vessel wall can be indicators of a patient at high riskof suffering a cardiovascular event, such as a heart attack. In certaincircumstances, areas where there exist areas of bad plaque can lead to arupture wherein there is a release of the fatty materials into thebloodstream of the artery, which in turn can cause a clot to develop inthe artery. A blood clot in the artery can cause a stoppage of bloodflow to the heart tissue, which can result in a heart attack.Accordingly, there is a need for new technology for analyzing arteryvessel walls and/or identifying areas within artery vessel walls thatcomprise a buildup of plaque whether it be bad or otherwise.

Various systems, methods, and devices disclosed herein are directed toembodiments for addressing the foregoing issues and to help educatepatients with respect to their particular state of cardiovasculardisease, state, or risk. In particular, in some embodiments, thesystems, devices, and methods are configured to generate an immersivepatient-specific report on the patient's cardiovascular disease risk,state, diagnosis, and/or treatment. In some embodiments, the systems,devices, and methods are configured to generate an immersivepatient-specific report based at least in part on image-based analysis,for example of one or more plaque and/or vessel parameters. In someembodiments, the systems, devices, and methods are configured to viewthe patient's cardiovascular disease state or risk from a point of viewwithin one or more arteries of the patient. In some embodiments, thesystems, devices, and methods are configured to graphically view and/ortrack actual or hypothetical progression of the patient's cardiovasculardisease state or risk based on actual or proposed treatment from a pointof view within one or more arteries of the patient. In some embodiments,the systems, devices, and methods are configured to generate animmersive virtual visitation experience in which patients may beconnected to medical professionals and view any of the generated reportsand/or graphical views as described above.

As discussed herein, disclosed herein are systems, methods, and devicesfor cardiovascular risk and/or disease state assessment usingimage-based analyses and/or generation of an immersive patient-specificreport using the same. In particular, in some embodiments, the systems,devices, and methods are configured to generate an immersivepatient-specific report on the patient's cardiovascular disease risk,state, diagnosis, and/or treatment. In some embodiments, the systems,devices, and methods are configured to generate an immersivepatient-specific report based at least in part on image-based analysis,for example of one or more plaque and/or vessel parameters.

In some embodiments, the systems, devices, and methods are configured toview the patient's cardiovascular disease state or risk from a point ofview within one or more arteries of the patient. For example, thesystems, methods, and devices may provide a patient and/or a doctor withan ability to view the patient's blood vessels in an AR and/or VRpresentations, for example, from within a blood vessel. This can allowthe patient and/or doctor to virtually tour the patient's vasculature,for example, to view potential dangerous plaques.

In some embodiments, the systems, devices, and methods are configured tographically view and/or track actual or hypothetical progression of thepatient's cardiovascular disease state or risk based on actual orproposed treatment from a point of view within one or more arteries ofthe patient.

Further, in some embodiments, the systems, methods, and devicesdescribed herein can allow for virtual consultations and/or physicianvisits, for example, in the metaverse. In particular, in someembodiments, the systems, methods, and devices described herein providea metaverse experience in which a patient is able to obtain a virtualconsultation with a physician prior to and/or after analysis of thepatient's health state and/or risk. In some embodiments, the systems,devices, and methods also provide a virtual consultation in themetaverse to go over the results of a patient-specific report. Forexample, in some embodiments, the systems, methods, and devices allowfor a virtual consultation while the patient is viewing an immersivepatient-specific report of the patient's coronary arteries using avirtual reality (VR) device and/or other computing device. In someembodiments, the systems, methods, and devices are also configured toprovide consultation, analysis, and/or generation of an immersivepatient-specific report on structural heart disease and/or valvularheart disease. More specifically, in some embodiments, the systems,methods, and devices are configured to allow a patient to view insidehis or her heart and/or valves to obtain a visceral experience of his orher state and/or risk of structural heart disease and/or valvular heartdisease.

As described herein, coronary artery disease (CAD) and/or othercardiovascular and/or coronary heart diseases affect millions ofpatients in the US alone. Further, by utilizing some systems, devices,and methods described herein, it is possible to quantify and/or moreaccurately assess the state and/or risk of disease in a subject. Forexample, some embodiments of systems, devices, and methods describedherein can be configured to analyze one or more medical images obtainedusing non-invasive measures to determine the extent and/or compositionof plaque, as well as other plaque parameters and/or vascularparameters. For example, in some embodiments, the system can beconfigured to utilize one or more artificial intelligence (AI) and/ormachine learning (ML) algorithms for determining such parameters. Basedon such analyses, in some embodiments, the system can be configured todetermine a risk of disease for a subject.

Despite such technical advantages and developments, however, in order toeffectively prevent and/or treat disease, it can be equally important toeducate patients, physicians, and/or subjects of individual state orrisk of disease, such as coronary disease. Effective education and/orreporting can significantly improve outcome, as subjects who have abetter understanding will likely adhere better to the proposedtreatment, which can include lifestyle changes, exercise, diet, and/orthe like. In order to better educate a subject on his or her state ofcoronary or cardiovascular disease, it can be important to personalizethe report. In addition, it can also be important to provide a more realand/or life-like report.

In order to address such goals and technical shortcomings of the currentstate of technology, some systems, devices, and methods described hereinare configured to generate an immersive, patient-specific report to eachsubject based on analyses of one or more plaque and/or vascularparameters derived from a medical image of that particular subject. Assuch, in some embodiments, the generated report can be specific to thepatient and can include patient-specific parameters, such as for exampletotal volume of plaque, volume of non-calcified plaque, volume of lowdensity plaque, volume of high density plaque, vascular remodeling,and/or the like.

Further, in some embodiments, the systems, methods, and devicesdescribed herein can be configured to provide an immersive experiencefor the subject, for example by generating an interactive and/orimmersive viewing experience of the subject's own arteries. Morespecifically, in some embodiments, the systems, methods, and devicesdescribed herein are configured to analyze one or more arteries of thesubject to determine the curvature of each artery and/or extent and typeof plaque within the one or more arteries. Based on such analyses, insome embodiments, the system can be configured to generate athree-dimensional graphical representation of the actual arteries of thesubject, including regions of plaque. In some embodiments, thethree-dimensional graphical representation can allow the subject oranother user to view his or her arteries from inside or within thearteries to obtain a visceral sense or immersive viewing experience ofhis or her arteries. Further, in some embodiments, the three-dimensionalgraphical representation can allow the subject or another user to movearound within the arteries, for example in six degrees of freedom(6-DOF) or three degrees of freedom (3-DOF).

In some embodiments, the three-dimensional graphical representation ofthe one or more arteries can include the one or more regions of plaquesuch that the subject or another user can view of the total extent ofplaque within his or her arteries and obtain a real or life-like sense.In some embodiments, each or some of the regions of plaque (or a portionthereof) can be color-coded based on the level of risk. For example, insome embodiments, a low-risk plaque or highly calcified or high-densityplaque can be assigned one color to depict the low risk of that plaque.Similarly, in some embodiments, certain plaques or regions of plaque ora portion thereof can comprise lower density and/or positive remodeling,in which case that plaque, region of plaque, or portion thereof can beassigned a different color. As such, in some embodiments, as the user is“moving” around his or her arteries, the user can see and obtain avisceral sense of how much total plaque, good plaque, and/or bad plaquethe user has, thereby increasing the impact of the patient-specificreport which can lead to better adherence to treatment and/or outcome.

Further, in some embodiments, the system can be configured to determinea risk of rupture of one or more regions of plaque, for example based onthe density, remodeling, and/or any other factor or parameter discussedherein. In some embodiments, if the system determines that the risk of aparticular plaque rupturing within a particular timeframe is higher thansome predetermined threshold, the system can be configured to animatethat plaque rupturing in the three-dimensional representation of the oneor more arteries of the subject. As such, in some embodiments, thesubject or user can be able to view a plaque rupturing within his or herartery, thereby further increasing the impact of the patient-specificreport.

In some embodiments, the systems, methods, and devices described hereincan be configured to generate one or more proposed treatments for thesubject based on the analyses of the one or more plaque and/or vesselparameters. In some embodiments, based on the generated proposedtreatment(s), the system can be configured to determine an expectedoutcome of the proposed treatment. For example, in some embodiments, thesystem may determine that the proposed treatment will likely harden orincrease the density of one or more plaques by a certain degree over acertain period of time. In some embodiments, the system can beconfigured to animate that change in plaque(s) in the three-dimensionalrepresentation of the one or more arteries of the subject. As such, insome embodiments, the subject can viscerally see the potential effectsof a proposed treatment.

In some embodiments, the systems, methods, and devices described hereincan be configured to generate an animation or graphical representationof what would happen to the subject's arteries without treatment. Forexample, in some embodiments, the system may determine that a subject isexpected to have a certain amount more of low-density plaque over acertain period of time without treatment. Based on such analyses, insome embodiments, the system can be configured to animate or generate agraphical representation of such change. In some embodiments, suchvisualization of change in plaque without treatment can be viewedside-by-side and/or in sequence with a visualization of change in plaquewith treatment, which can be an effective way to educate the subjectand/or improve adherence to treatment.

In some embodiments, the systems, methods, and devices described hereincan be configured to track progression of plaque or disease, either withor without treatment. For example, in some embodiments, the systems,methods, and devices can be configured to generate and/or update agraphical representation of the arteries of the subject at a secondpoint in time to track progression of disease or plaque. In someembodiments, if no particular treatment was given to the subject, thesystem can be configured to track progression of plaque and provide avisual representation of the same to the subject. In some embodiments,if a treatment was assigned to the subject, whether invasive, lifestylechange, or drug treatment, the system can be configured to graphicallyshow the subject the difference if plaque or disease, thereby allowingthe subject to visually track his or her progress.

As such, in some embodiments, the systems, methods, and devicesdescribed herein can provide an immersive patient-specific view frominside or within the subject's own arteries, thereby providing thesubject and/or a physician with a visceral view or sense of the stateand/or progression of plaque or disease or CAD. In some embodiments, theimmersive patient-specific report can be viewable and/or experiencedusing a virtual reality (VR) device or headset and/or other computingdevice. In some embodiments, the immersive patient-specific report caninclude a voiceover or narrative to guide the user as the user exploresaround his or her arteries. In some embodiments, the immersivepatient-specific report can include one or more sound and/or motioneffects, for example associated with when a plaque ruptures, such that aVR headset rumbles or moves otherwise for added effect when a plaqueruptures. In some embodiments, the immersive patient-specific report caninclude one or more areas that the user can click or otherwise select,which can lead to additional menus or features or descriptions. Forexample, in some embodiments, if a user selects a region of plaque, theimmersive patient-specific report can be configured to notify the user,either orally or graphically or via text, that the region corresponds toplaque, fat, specific type of plaque, and/or the like. In someembodiments, such immersive patient-specific report can have asignificant impact on patient education and/or adherence to treatmentand/or improved treatment outcome compared to a more traditionalpaper-based or document report with standard boilerplate description orlanguage.

Further, in some embodiments, the systems, methods, and devicesdescribed herein can allow for virtual consultations and/or physicianvisits, for example, in the metaverse. In particular, in someembodiments, the systems, methods, and devices described herein canprovide an immersive virtual consultation and/or visit using a virtualreality (VR) device headset and/or other computing device. In someembodiments, the systems, methods, and devices described herein canallow a patient and physician to communicate using a VR device headsetand/or other computing device. In some embodiments, the systems,methods, and devices described herein provide a metaverse experience inwhich a patient is able to obtain a virtual consultation with aphysician through use of avatars rendered in a VR space or otherwise. Insome embodiments, the systems, methods, and devices described hereinallow avatars of a patient and physician to move, speak, or otherwiseinteract in a virtual consultation setting.

In particular, in some embodiments, the systems, methods, and devicesdescribed herein provide a metaverse experience in which a patient isable to obtain a virtual consultation with a physician prior to and/orafter analysis of the patient's health state and/or risk. In someembodiments, the systems, devices, and methods also provide a virtualconsultation in the metaverse to go over the results of apatient-specific report. For example, in some embodiments, the systems,methods, and devices allow for a virtual visitation in which a physicianmay allow for a virtual consultation while the patient is viewing animmersive patient-specific report of the patient's coronary arterieswhile in the metaverse. For example, in some embodiments, the systems,methods, and devices allow for a virtual consultation while the patientis viewing an immersive patient-specific report of the patient'scoronary arteries using a virtual reality (VR) device and/or othercomputing device.

FIG. 13A is a schematic illustrating an example of an embodiment(s) ofan immersive patient-specific report on cardiovascular disease risk,state, diagnosis, and/or treatment that can be generated using one ormore embodiments of the systems, methods, and devices described herein.

As illustrated in FIG. 13A, in some embodiments, the systems, methods,and devices described herein can be configured to analyze one or morevessels, such as artery 1300A on a medical image. The one or morevessels, such as artery 1300A can taper along a longitudinal axis 1314Asuch that the diameter of the vessel generally decreases. In someembodiments, artery 1300A can include one or more areas of plaque 1302A,1304A, 1312A. Further, an area or region of plaque 1302A can furtherinclude one or more regions of plaque 1306A, 1308A, 1310A with differentplaque compositions. Each or some of the one or more areas of plaque1302A, 1304A, 1312A and/or portions within a region of plaque 1306A,1308A, 1310A can comprise a different plaque composition. For example,one or more regions of plaque can comprise low-density plaque,low-density non-calcified plaque, and/or high-density plaque. In someembodiments, the system can be configured to analyze the composition ofplaque utilizing one or more image analysis techniques. In someembodiments, the system can be configured to characterize thecomposition of plaque based on the absolute density and/or relativedensity, radiodensity, and/or Hounsfield unit of one or more pixelsshown on a medical image of plaque.

In some embodiments, the system can be configured to analyze,characterize, and/or classify plaque into one of three types:low-density non-calcified plaque, non-calcified plaque, and calcifiedplaque. In some embodiments, the system can be configured to analyze,characterize, and/or classify plaque into one of two types:non-calcified plaque and calcified plaque. In some embodiments, thesystem can be configured to analyze, characterize, and/or classifyplaque into one of any number of different types, such as for example 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, and/or 30 different types ofclassifications of plaque. In some embodiments, the system can beconfigured to analyze, characterize, and/or classify plaque into anumber of different types within a range defined by two of theaforementioned values.

In some embodiments, as described herein, the system can be configuredto generate an immersive patient-specific report of the patient'sarteries. For example, referring to FIG. 13A as an illustrative example,in some embodiments, the immersive patient-specific report can comprisea graphical representation of an artery 1300A of the subject, which canfurther include one or more regions of plaque 1302A, 1304A, 1312A. Insome embodiments, the immersive patient-specific report can comprise agraphical representation of one or more portions of plaque 1306A, 1308A,1310A within a region of plaque 1302A. In some embodiments, depending onthe type or composition of plaque, the system can be configured toannotate or color each region of plaque 1302A, 1304A, 1306A, 1308A,1310A, 1312A. As such, in some embodiments, the user can view eachregion of plaque and know instantaneously the risk factor associatedwith each region of plaque.

In some embodiments, as described herein, the system can allow a user toview the immersive patient-specific report from a point of view 1316Awithin an artery. As such, in some embodiments, the user or subject canview from inside his or her artery different regions of plaque, fat,and/or vessel in 3-DOF. In some embodiments, the immersivepatient-specific report can allow the user or subject to move aroundwithin the artery 1300A, for example in 6-DOF and/or along thelongitudinal axis 1316A. In some embodiments, the system can beconfigured to utilize one or more coordinate systems for processingmovement and/or rotation. For example, in some embodiments, the systemcan be configured to utilize a cartesian coordinate system, polarcoordinate system, cylindrical coordinate system, spherical coordinatesystem, homogeneous coordinate system, curvilinear coordinate system,log-polar coordinate system, Plucker coordinate system, generalizedcoordinate system, canonical coordinate system, barycentric coordinatesystem, and/or trilinear coordinate system.

FIG. 13B is a schematic illustrating an example of an embodiment(s) ofan immersive patient-specific report on cardiovascular disease risk,state, diagnosis, and/or treatment that can be generated using one ormore embodiments of the systems, methods, and devices described herein.As illustrated in FIG. 13B, in some embodiments, the system can beconfigured to allow a user to travel or move across different arteries,such as for example the left coronary artery, right coronary artery,right (acute) marginal artery, circumflex artery, left (obtuse) marginalartery, left anterior descending artery, and/or diagonal arteries. Insome embodiments, the system can be configured to generate athree-dimensional graphical representation of all or some of thearteries. In some embodiments, the system can allow a user to select orclick into a particular artery, which can then allow the user to movearound or view the inside of that particular artery. In someembodiments, to create a three-dimensional graphical representation ofone or more arteries, the system can be configured to analyze one ormore different slices, such as for example axial, coronal, and/orsagittal views. In some embodiments, the system can be configured toconstruct a multi-planar reformat or reconstruct of the different slicesto generate a three-dimensional graphical representation of one or morearteries. In some embodiments, the system can be configured to add orsuperimpose one or more plaque and/or fat features onto thethree-dimensional graphical representation of one or more arteries ormulti-planar reformat or reconstruct. As such, in some embodiments,using non-invasive medical imaging techniques, the system can beconfigured to generate a three-dimensional representation of one or moreactual arteries of the subject, including plaque and/or fat featuresand/or risk thereof, and provide an immersive viewing experience for thesubject from within the vessel or lumen wall of one or more arteries.

Immersive Patient-Specific Report Generation

As described herein, some embodiments of systems, devices, and methodsdescribed herein are configured to generate an immersivepatient-specific report on cardiovascular disease risk, state, and/ortreatment, for example using image-based analysis of one or more plaqueand/or vessel parameters derived from a medical image. FIG. 13C is aflowchart illustrating an example embodiment(s) of systems, devices, andmethods for generation of an immersive patient-specific report oncardiovascular disease risk, state, diagnosis, and/or treatment.

As illustrated in FIG. 13C, in some embodiments, the system can beconfigured to access a medical image at block 1302C. In someembodiments, the medical image can include one or more arteries, such ascoronary, carotid, and/or other arteries of a subject. In someembodiments, the medical image can be stored in a medical image database1304C. In some embodiments, the medical image database 1304C can belocally accessible by the system and/or can be located remotely andaccessible through a network connection. The medical image can comprisean image obtain using one or more modalities such as for example, CT,Dual-Energy Computed Tomography (DECT), Spectral CT, photon-counting CT,x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS),Magnetic Resonance (MR) imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS). In some embodiments, the medical image comprisesone or more of a contrast-enhanced CT image, non-contrast CT image, MRimage, and/or an image obtained using any of the modalities describedabove.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 1306C, thesystem can be configured to identify one or more vessels on the medicalimage(s), such as of one or more arteries. The one or more arteries caninclude coronary arteries, carotid arteries, aorta, renal artery, lowerextremity artery, upper extremity artery, and/or cerebral artery,amongst others. In some embodiments, the system can be configured toutilize one or more artificial intelligence (AI) and/or machine learning(ML) algorithms to automatically and/or dynamically identify one or morearteries or coronary arteries using image processing. For example, insome embodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich arteries or coronary arteries have been identified, therebyallowing the AI and/or ML algorithm automatically identify arteries orcoronary arteries directly from a medical image. In some embodiments,the arteries or coronary arteries are identified by size and/orlocation.

In some embodiments, at block 1308C, the system can be configured toidentify one or more regions of plaque, one or more regions of fat,and/or one or more other vessel features. In some embodiments, thesystem can be configured to utilize one or more AI and/or ML algorithmsto automatically and/or dynamically identify and/or characterize one ormore regions of plaque, one or more regions of fat, and/or one or moreother vessel features, for example using image processing. In someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich one or more regions of plaque, one or more regions of fat, and/orone or more other vessel features have been identified and/orcharacterized, thereby allowing the AI and/or ML algorithm toautomatically identify and/or characterize the same directly from amedical image.

In some embodiments, at block 1310C, the system can be configured toanalyze one or more plaque parameters, one or more vessel parameters,one or more fat parameters, and/or one or more relational parameters,for example on one or more two-dimensional slices or images and/or onone or more three-dimensional images. In some embodiments, the systemcan be configured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically identify and/or determine one or moreone or more plaque parameters, one or more vessel parameters, one ormore fat parameters, and/or one or more relational parameters. Forexample, in some embodiments, the one or more AI and/or ML algorithmscan be trained using a Convolutional Neural Network (CNN) on a set ofmedical images on which one or more plaque parameters, one or morevessel parameters, one or more fat parameters, and/or one or morerelational parameters have been determined, thereby allowing the AIand/or ML algorithm to automatically determine the same directly from amedical image.

In some embodiments, the system can be configured to analyze theabsolute density, relative density, radiodensity values, and/orheterogeneity or distribution thereof of one or more pixels on a medicalimage to identify and/or characterize plaque. More specifically, in someembodiments, the system can be configured to identify a particular pixelas plaque, non-calcified plaque, low-density non-calcified plaque,and/or calcified plaque, and/or any other classification or type ofplaque. In some embodiments, the system can be configured to classify aparticular pixel or region of plaque as non-calcified plaque when theradiodensity value of the pixel is below a certain predeterminedthreshold, within a predetermined range, and/or comprises aheterogeneity index or distribution within, under, or above a particularpredetermined range or threshold. In some embodiments, the system can beconfigured to classify a particular pixel or region of plaque aslow-density non-calcified plaque when the radiodensity value of thepixel is below a certain predetermined threshold, within a predeterminedrange, and/or comprises a heterogeneity index or distribution within,under, or above a particular predetermined range or threshold. In someembodiments, the system can be configured to classify a particular pixelor region of plaque as calcified plaque when the radiodensity value ofthe pixel is above a certain predetermined threshold, within apredetermined range, and/or comprises a heterogeneity index ordistribution within, under, or above a particular predetermined range orthreshold.

In some embodiments, the one or more plaque parameters can includeabsolute plaque density, relative plaque density, composition,calcification, radiodensity, location, volume, surface area, geometry,heterogeneity, diffusivity, and/or ratio between volume and surfacearea. In some embodiments, the one or more fat parameters can includefat density, relative fat density, composition, radiodensity, location,volume, surface area, geometry, heterogeneity, diffusivity, and/or ratiobetween volume and surface area. In some embodiments, the one or morevessel or vascular parameters can include one or more of vascularvolume, diameter, area, cross-sectional area, surface area, length,location, and/or remodeling. In some embodiments, the one or morerelational plaque parameters can include a ratio or other comparisonbetween one or more plaque parameters, one or more vessel parameters,and/or one or more fat parameters. For example, in some embodiments, theone or more relational parameter can include percent atheroma volume(PAV), PAV on a vessel-by-vessel basis, PAV on a segment-by-segmentbasis, and/or PAV for the whole heart, ratio of surface area of plaqueto surface of vessel or lumen, ratio of volume of plaque to volume ofvessel or lumen, and/or the like.

In some embodiments, at block 1312C, the system can be configured todetermine a risk or state of cardiovascular disease or health of one ormore regions of plaque and/or the subject, for example based on one ormore plaque parameters, one or more vessel parameters, one or more fatparameters, and/or one or more relational parameters derived from themedical image. Further, in some embodiments, at block 1312C, the systemcan be configured to determine a proposed treatment for one or moreregions of plaque and/or the subject. The treatment can include, forexample, medical treatment such as statins, interventional treatmentsuch as stent implantation, and/or lifestyle treatment such as exerciseor diet.

In some embodiments, in determining the risk or state of cardiovasculardisease or health and/or treatment, the system can access a plaquerisk/treatment database 1314C, which can be locally accessible by thesystem and/or can be located remotely and accessible through a networkconnection. In some embodiments, the plaque risk/treatment database1314C can include reference points or data that relate one or moretreatment to cardiovascular disease risk or state determined based onone or more plaque parameters, one or more vessel parameters, one ormore fat parameters, and/or one or more relational parameters.

In some embodiments, the system can be configured to determine the riskor state of cardiovascular disease or health of one or more regions ofplaque and/or the subject based on a weighted measure of the one or moreplaque parameters, one or more vessel parameters, one or more fatparameters, and/or one or more relational parameters. For example, insome embodiments, the system can be configured to assign a weightbetween 0 and 1 to one or more plaque parameters, one or more vesselparameters, one or more fat parameters, and/or one or more relationalparameters. In some embodiments, the system can be configured to assigna weight between 0 and 1, such as for example 0, 0.05, 0.1, 0.15, 0.2,0.3, 0.4, 0.5. 0.6, 0.7, 0.8, 0.9, 0.99, and 1. In some embodiments, thesystem can be configured to weight one or more parameters as 0, therebyignoring its effect. In some embodiments, the system can be configuredto weight one or more parameters as 1, thereby focusing exclusively onsuch parameter. In some embodiments, the weighted measure can alsoinclude one or more other factors or features, such as for example, age,weight, gender, plaque volume, plaque composition, vascular remodeling,high-risk plaque, lumen volume, plaque location (proximal v. middle v.distal), plaque location (myocardial v. pericardial facing), plaquelocation (at bifurcation or trifurcation v. not at bifurcation ortrifurcation), plaque location (in main vessel v. branch vessel),stenosis severity, percentage coronary blood volume, percentagefractional myocardial mass, percentile for age and/or gender, constantor other correction factor to allow for control of within-person,within-vessel, inter-plaque, plaque-myocardial relationships, and/or thelike.

In some embodiments, at block 1316C, the system can be configured togenerate a multiplanar reformation or multiplanar reconstruction (MPR)view of the one or more arteries. For example, in some embodiments, thesystem can be configured to analyze a plurality of two-dimensionalslices or images obtained from the subject. In some embodiments, thesystem can be configured to analyze the plurality of two-dimensionalslices or images to reconstruct a multiplanar view of one or morearteries in three-dimensions. For example, in some embodiments, thesystem can be configured to utilize any one or more coordinate systemsdescribed herein in generating the MPR view.

In some embodiments, at block 1318C, the system can be configured togenerate and/or transmit a still immersive view to a user computingdevice. For example, in some embodiments, the system can be configuredto generate an immersive two-dimensional or three-dimensional view ofthe inside of one or more arteries or other vessels, including one ormore regions of plaque and/or fat, using the MPR view. In someembodiments, the immersive view can be configured to allow a user toexplore and/or move around a graphical representation of the one or morearteries or vessels, for example in 3-DOF or 6-DOF. In some embodiments,the still immersive view can include one or more actual curvatures ofthe one or more arteries or other vessels of the subject. In someembodiments, the still immersive view can include an audio narrativedescribing different features within the one or more arteries orvessels. In some embodiments, the audio narrative can map and/or followmovement or view of the user inside the one or arteries or vessels. Forexample, in some embodiments, the system can be configured to play acertain audio narrative based on what the user is focusing on within theimmersive view of the artery or vessel.

In some embodiments, at block 1320C, the system can be configured todetermine one or more potential plaque scenarios, for example based onthe risk assessment of one or more plaques. More specifically, in someembodiments, based on the risk assessment of one or more regions ofplaque, the system can be configured to determine the likelihood of aparticular plaque rupturing within a particular period of time, such asfor example about 1 year, about 2 years, about 3 years, about 4 years,about 5 years, about 6 years, about 7 years, about 8 years, about 9years, about 10 years, about 15 years, about 20 years, and/or within arange defined by two of the aforementioned values.

In some embodiments, at block 1322C, the system can be configured todetermine one or more flow characteristics within the vessel, such asfor example fractional flow reserve (FFR). In some embodiments, thesystem can be configured to determine one or more flow characteristicsusing the risk assessment of one or more regions of plaque, boundaryconditions of the vessel or lumen wall, curvature of the vessel or lumenwall, and/or the like.

In some embodiments, at block 1324C, the system can be configured togenerate and/or transmit a dynamic immersive view to a user computingdevice. For example, in some embodiments, the system can be configuredto generate a dynamic immersive two-dimensional or three-dimensionalview of the inside of one or more arteries or other vessels, includingone or more regions of plaque and/or fat, using the MPR view. In someembodiments, the dynamic immersive view can include one or moredynamically moving features within the vessel. For example, in someembodiments, the dynamic immersive view can include a graphicalrepresentation of blood flow, plaque rupturing, and/or the like, toallow the user to obtain a dynamic view and/or experience of whathappens and/or what can happen inside his or her arteries or vessels. Inparticular, in some embodiments, the system can be configured togenerate a graphical representation of a particular plaque rupturingif/when the likelihood of such plaque rupturing is determined to beabove a particular threshold, for example utilizing one or moreprocesses described in relation to block 1320C. Further, in someembodiments, the system can be configured to generate a graphicalrepresentation of blood flow through the vessel based at least in parton one or more flow characteristics determined utilizing one or moreprocesses described in relation to block 1322C. In some embodiments, thesystem can be configured to generate a graphical representation of aplaque rupturing and how that affects the blood flow within that vesselor artery.

In some embodiments, the dynamic immersive view can be configured toallow a user to explore and/or move around a graphical representation ofthe one or more arteries or vessels, for example in 3-DOF or 6-DOF. Insome embodiments, the dynamic immersive view can include one or moreactual curvatures of the one or more arteries or other vessels of thesubject. In some embodiments, the dynamic immersive view can include anaudio narrative describing different features within the one or morearteries or vessels. In some embodiments, the audio narrative can mapand/or follow movement or view of the user inside the one or arteries orvessels. For example, in some embodiments, the system can be configuredto play a certain audio narrative based on what the user is focusing onwithin the dynamic immersive view of the artery or vessel.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1302C-1324C, for example forone or more other vessels, segments, regions of plaque, other subjects,and/or for the same subject at a different time. For example, in someembodiments, the system can be configured to repeat one or moreprocesses after the same subject is treated with a particular treatmentso that the subject can be provided a graphical/visual representation ofthe actual effects of treatment. As such, in some embodiments, thesystem can provide for longitudinal disease tracking and/or personalizedtreatment for a subject. Similarly, in some embodiments, the system canbe configured to repeat one or more processes for the same subject afterhypothetically treating the subject with a particular treatment, therebyproviding the subject with a graphical/visual representation of expectedoutcomes of a particular treatment.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 13D. The example computer system 1302D is incommunication with one or more computing systems 1320D and/or one ormore data sources 1322D via one or more networks 1318D. While FIG. 13Dillustrates an embodiment of a computing system 1302D, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1302D can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1302D can comprise an Immersive Patient-SpecificReport Generation Module 1314D that carries out the functions, methods,acts, and/or processes described herein. The Immersive Patient-SpecificReport Generation Module 1314D executed on the computer system 1302D bya central processing unit 1306D discussed further below. Other featuresof the computer system 1302D can be similar to corresponding features ofthe computer system of FIG. 9G, described above.

Certain Examples of Embodiments of Immersive Patient-Specific Report

The following are non-limiting examples of certain embodiments ofsystems and methods of immersive patient-specific report. Otherembodiments may include one or more other features, or differentfeatures, that are discussed herein.

Embodiment 1: A computer-implemented method of generating an immersivepatient-specific report on cardiovascular disease state based on one ormore plaque parameters derived from non-invasive medical image analysis,the method comprising: accessing, by a computer system, a medical imageof a subject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries, wherein the one or morearteries comprise one or more regions of plaque; determining, by thecomputer system, one or more vascular parameters associated with thesubject by analyzing the one or more identified arteries, wherein theone or more vascular parameters comprise one or more of vessel volume,diameter, area, cross-sectional area, surface area, length, location, orremodeling; identifying, by the computer system, the one or more regionsof plaque in the one or more arteries; determining, by the computersystem, one or more plaque parameters associated with the one or moreregions of plaque, wherein the one or more plaque parameters compriseone or more of plaque density, composition, calcification, radiodensity,location, volume, surface area, geometry, heterogeneity, diffusivity, orratio between volume and surface area; generating, by the computersystem, an assessment of cardiovascular disease state of the one or moreregions of plaque based at least in part on the determined one or moreplaque parameters and the one or more vascular parameters; generating,by the computer system, a three-dimensional graphical representation ofmyocardial infarction arising from the one or more regions of plaquewhen the generated assessment of cardiovascular disease state of the oneor more regions of plaque is above a pre-determined threshold level;generating, by the computer system, a three-dimensional multiplanarreformation of the one or more arteries comprising the one or moreregions of plaque based at least in part on the determined one or morevascular parameters and the one or more plaque parameters; generating,by the computer system, an immersive three-dimensional graphicalrepresentation of the one or more arteries based at least in part on thethree-dimensional multiplanar reformation of the one or more arteries,wherein the immersive three-dimensional graphical representation of theone or more arteries comprises the three-dimensional graphicalrepresentation of the myocardial infarction arising from the one or moreregions of plaque when the generated assessment of cardiovasculardisease state of the one or more regions of plaque is above apre-determined threshold level; and causing, by the computer system,transmission of the immersive three-dimensional graphical representationof the one or more arteries to a user computing device, wherein theimmersive three-dimensional graphical representation of the one or morearteries is configured to allow a user to view the state ofcardiovascular disease from a point of view positioned inside the one ormore arteries, wherein the computer system comprises a computerprocessor and an electronic storage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinthe user computing device comprises a virtual reality (VR) device.

Embodiment 3: The computer-implemented method of Embodiment 1, whereinthe pre-determined threshold level is based at least in part on densityof the one or more regions of plaque.

Embodiment 4: The computer-implemented method of Embodiment 1, whereinthe density of the one or more regions of plaque comprises absolutedensity.

Embodiment 5: The computer-implemented method of Embodiment 1, whereinthe density of the one or more regions of plaque comprises radiodensity.

Embodiment 6: The computer-implemented method of Embodiment 1, whereinthe immersive three-dimensional graphical representation of the one ormore arteries is configured to allow a user to move the point of viewwithin the one or more arteries in six degrees of freedom.

Embodiment 7: The computer-implemented method of Embodiment 1, whereinthe immersive three-dimensional graphical representation of the one ormore arteries is configured to allow a user to rotate the point of viewwithin the one or more arteries in three degrees of freedom.

Embodiment 8: The computer-implemented method of Embodiment 1, whereinthe immersive three-dimensional graphical representation of the one ormore arteries is configured to allow a user to move the point of viewwithin the one or more arteries along a longitudinal axis of the one ormore arteries.

Embodiment 9: The computer-implemented method of Embodiment 1, furthercomprising generating, by the computer system, a treatment forcardiovascular disease for the subject based at least in part on thedetermined assessment of the state of cardiovascular disease of the oneor more regions of plaque.

Embodiment 10: The computer-implemented method of Embodiment 9, furthercomprising: determining, by the computer system, an expected progressionof the state of cardiovascular disease of the one or more regions ofplaque based on the treatment; modifying, by the computer system, theimmersive three-dimensional graphical representation of the one or morearteries based at least in part on the expected progression of the stateof cardiovascular disease of the one or more regions of plaque; andcausing, by the computer system, transmission of the modified immersivethree-dimensional graphical representation of the one or more arteriesto the user computing device, wherein the modified immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow the user to view the expected progression of thestate of cardiovascular disease of the one or more regions of plaquefrom the point of view positioned within the one or more arteries.

Embodiment 11: The computer-implemented method of Embodiment 9, whereinthe treatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.

Embodiment 12: The computer-implemented method of Embodiment 9, furthercomprising tracking, by the computer system, efficacy of the treatmentby determining assessment of the state of cardiovascular disease of theone or more regions of plaque at a later point in time after treatment.

Embodiment 13: The computer-implemented method of Embodiment 12, furthercomprising: modifying, by the computer system, the immersivethree-dimensional graphical representation of the one or more arteriesbased at least in part on the assessment of the state of cardiovasculardisease of the one or more regions of plaque at the later point in timeafter treatment of the state of cardiovascular disease; and causing, bythe computer system, transmission of the modified immersivethree-dimensional graphical representation of the one or more arteriesto the user computing device, wherein the modified immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow the user to view a change in the state ofcardiovascular disease of the one or more regions of plaque aftertreatment from the point of view positioned within the one or morearteries.

Embodiment 14: The computer-implemented method of Embodiment 1, whereinthe state of assessment of cardiovascular disease state of the one ormore regions of plaque based at least in part on a weighted measure ofthe one or more plaque parameters and the one or more vascularparameters.

Embodiment 15: The computer-implemented method of Embodiment 1, furthercomprising: generating, by the computer system, a weighted measure ofone or more of the one or more vascular parameters and the one or moreplaque parameters, wherein the assessment of the state of cardiovasculardisease of the one or more regions of plaque is further determined basedat least in part on the generated weighted measure.

Embodiment 16: The computer-implemented method of Embodiment 1, whereinthe assessment of the state of cardiovascular disease of the one or moreregions of plaque is further determined based at least in part on one ormore of age or gender of the subject.

Embodiment 17: The computer-implemented method of Embodiment 1, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).

Embodiment 18: The computer-implemented method of Embodiment 1, whereinthe myocardial infarction arises from one or more of plaque rupture,plaque erosion or calcified nodule.

Embodiment 19: A system for generating an immersive patient-specificreport on cardiovascular disease state based on one or more plaqueparameters derived from non-invasive medical image analysis, the systemcomprising: one or more computer readable storage devices configured tostore a plurality of computer executable instructions; and one or morehardware computer processors in communication with the one or morecomputer readable storage devices and configured to execute theplurality of computer executable instructions in order to cause thesystem to: access a medical image of a subject, wherein the medicalimage of the subject is obtained non-invasively; analyze the medicalimage of the subject to identify one or more arteries, wherein the oneor more arteries comprise one or more regions of plaque; determine oneor more vascular parameters associated with the subject by analyzing theone or more identified arteries, wherein the one or more vascularparameters comprise one or more of vessel volume, diameter, area,cross-sectional area, surface area, length, location, or remodeling;identify the one or more regions of plaque in the one or more arteries;determine one or more plaque parameters associated with the one or moreregions of plaque, wherein the one or more plaque parameters compriseone or more of plaque density, composition, calcification, radiodensity,location, volume, surface area, geometry, heterogeneity, diffusivity, orratio between volume and surface area; generate an assessment ofcardiovascular disease state of the one or more regions of plaque basedat least in part on the determined one or more plaque parameters and oneor more vascular parameters; generate a three-dimensional graphicalrepresentation of myocardial infarction arising from the one or moreregions of plaque when the generated assessment of cardiovasculardisease state of the one or more regions of plaque is above apre-determined threshold level; generate a three-dimensional multiplanarreformation of the one or more arteries comprising the one or moreregions of plaque based at least in part on the determined one or morevascular parameters and the one or more plaque parameters; generate animmersive three-dimensional graphical representation of the one or morearteries based at least in part on the three-dimensional multiplanarreformation of the one or more arteries, wherein the immersivethree-dimensional graphical representation of the one or more arteriescomprises the three-dimensional graphical representation of themyocardial infarction arising from the one or more regions of plaquewhen the generated assessment of cardiovascular disease state of the oneor more regions of plaque is above a pre-determined threshold level; andcause transmission of the immersive three-dimensional graphicalrepresentation of the one or more arteries to a user computing device,wherein the immersive three-dimensional graphical representation of theone or more arteries is configured to allow a user to view the state ofcardiovascular disease from a point of view positioned inside the one ormore arteries.

Embodiment 20: The system of Embodiment 19, wherein the user computingdevice comprises a virtual reality (VR) device.

Embodiment 21: The system of Embodiment 19, wherein the pre-determinedthreshold level is based at least in part on density of the one or moreregions of plaque.

Embodiment 22: The system of Embodiment 19, wherein the density of theone or more regions of plaque comprises absolute density.

Embodiment 23: The system of Embodiment 19, wherein the density of theone or more regions of plaque comprises radiodensity.

Embodiment 24: The system of Embodiment 19, wherein the immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow a user to move the point of view within the oneor more arteries in six degrees of freedom.

Embodiment 25: The system of Embodiment 19, wherein the immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow a user to rotate the point of view within the oneor more arteries in three degrees of freedom.

Embodiment 26: The system of Embodiment 19, wherein the immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow a user to move the point of view within the oneor more arteries along a longitudinal axis of the one or more arteries.

Embodiment 27: The system of Embodiment 19, wherein the system isfurther caused to generate a treatment for cardiovascular disease forthe subject based at least in part on the determined assessment of thestate of cardiovascular disease of the one or more regions of plaque.

Embodiment 28: The system of Embodiment 27, wherein the system isfurther caused to: determine an expected progression of the state ofcardiovascular disease of the one or more regions of plaque based on thetreatment; modify the immersive three-dimensional graphicalrepresentation of the one or more arteries based at least in part on theexpected progression of the state of cardiovascular disease of the oneor more regions of plaque; and cause transmission of the modifiedimmersive three-dimensional graphical representation of the one or morearteries to the user computing device, wherein the modified immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow the user to view the expected progression of thestate of cardiovascular disease of the one or more regions of plaquefrom the point of view positioned within the one or more arteries.

Embodiment 29: The system of Embodiment 27, wherein the treatment forcardiovascular disease comprises medical intervention, medicaltreatment, or lifestyle change.

Embodiment 30: The system of Embodiment 27, wherein the system isfurther caused to track efficacy of the treatment by determiningassessment of the state of cardiovascular disease of the one or moreregions of plaque at a later point in time after treatment.

Embodiment 31: The system of Embodiment 30, wherein the system isfurther caused to: modify the immersive three-dimensional graphicalrepresentation of the one or more arteries based at least in part on theassessment of the state of cardiovascular disease of the one or moreregions of plaque at the later point in time after treatment of thestate of cardiovascular disease; and cause transmission of the modifiedimmersive three-dimensional graphical representation of the one or morearteries to the user computing device, wherein the modified immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow the user to view a change in the state ofcardiovascular disease of the one or more regions of plaque aftertreatment from the point of view positioned within the one or morearteries.

Embodiment 32: The system of Embodiment 19, wherein the state ofassessment of cardiovascular disease state of the one or more regions ofplaque based at least in part on a weighted measure of the one or moreplaque parameters and the one or more vascular parameters.

Embodiment 33: The system of Embodiment 19, wherein the system isfurther caused to: generate a weighted measure of one or more of the oneor more vascular parameters and the one or more plaque parameters,wherein the assessment of the state of cardiovascular disease of the oneor more regions of plaque is further determined based at least in parton the generated weighted measure.

Embodiment 34: The system of Embodiment 19, wherein the assessment ofthe state of cardiovascular disease of the one or more regions of plaqueis further determined based at least in part on one or more of age orgender of the subject.

Embodiment 35: The system of Embodiment 19, wherein the medical image isobtained using an imaging technique comprising one or more of computedtomography (CT), x-ray, ultrasound, echocardiography, intravascularultrasound (IVUS), MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS).

Embodiment 36: The system of Embodiment 19, wherein the myocardialinfarction arises from one or more of plaque rupture, plaque erosion orcalcified nodule.

Normalized Plaque Parameters Introduction

Various systems, methods, and devices disclosed herein are directed toembodiments for addressing the foregoing issues. In particular, variousembodiments described herein relate to systems, methods, and devices forcardiovascular risk, disease, and/or state assessment using image-basedanalyses. In particular, in some embodiments, the systems, devices, andmethods are related to cardiovascular risk and/or disease and/or stateassessment using normalized image analysis-based plaque parameters. Insome embodiments, assessment of cardiovascular risk and/or diseaseand/or state generated using the systems, methods, and devices hereincan be utilized to diagnose and/or generate a proposed treatment for apatient.

In some embodiments, the systems, devices, and methods described hereinare configured to utilize non-invasive medical imaging technologies,such as a CT image for example, which can be inputted into a computersystem configured to automatically and/or dynamically analyze themedical image to identify one or more coronary arteries and/or plaquewithin the same. For example, in some embodiments, the system can beconfigured to utilize one or more machine learning and/or artificialintelligence algorithms to automatically and/or dynamically analyze amedical image to identify, quantify, and/or classify one or morecoronary arteries and/or plaque. In some embodiments, the system can befurther configured to utilize the identified, quantified, and/orclassified one or more coronary arteries and/or plaque to generate atreatment plan, track disease progression, and/or a patient-specificmedical report, for example using one or more artificial intelligenceand/or machine learning algorithms In some embodiments, the system canbe further configured to dynamically and/or automatically generate avisualization of the identified, quantified, and/or classified one ormore coronary arteries and/or plaque, for example in the form of agraphical user interface. Further, in some embodiments, to calibratemedical images obtained from different medical imaging scanners and/ordifferent scan parameters or environments, the system can be configuredto utilize a normalization device comprising one or more compartments ofone or more materials.

As will be discussed in further detail, the systems, devices, andmethods described herein allow for automatic and/or dynamic quantifiedanalysis of various parameters relating to plaque, cardiovasculararteries, and/or other structures. More specifically, in someembodiments described herein, a medical image of a patient, such as acoronary CT image, can be taken at a medical facility. Rather thanhaving a physician eyeball or make a general assessment of the patient,the medical image is transmitted to a backend main server in someembodiments that is configured to conduct one or more analyses thereofin a reproducible manner. As such, in some embodiments, the systems,methods, and devices described herein can provide a quantifiedmeasurement of one or more features of a coronary CT image usingautomated and/or dynamic processes. For example, in some embodiments,the main server system can be configured to identify one or morevessels, plaque, and/or fat from a medical image. Based on theidentified features, in some embodiments, the system can be configuredto generate one or more quantified measurements from a raw medicalimage, such as for example radiodensity of one or more regions ofplaque, identification of stable plaque and/or unstable plaque, volumesthereof, surface areas thereof, geometric shapes, heterogeneity thereof,and/or the like. In some embodiments, the system can also generate oneor more quantified measurements of vessels from the raw medical image,such as for example diameter, volume, morphology, and/or the like. Basedon the identified features and/or quantified measurements, in someembodiments, the system can be configured to generate a risk and/ordisease state assessment and/or track the progression of a plaque-baseddisease or condition, such as for example atherosclerosis, stenosis,and/or ischemia, using raw medical images. Further, in some embodiments,the system can be configured to generate a visualization of GUI of oneor more identified features and/or quantified measurements, such as aquantized color mapping of different features. In some embodiments, thesystems, devices, and methods described herein are configured to utilizemedical image-based processing to assess for a subject his or her riskof a cardiovascular event, major adverse cardiovascular event (MACE),rapid plaque progression, and/or non-response to medication. Inparticular, in some embodiments, the system can be configured toautomatically and/or dynamically assess such health risk of a subject byanalyzing only non-invasively obtained medical images. In someembodiments, one or more of the processes can be automated using anartificial intelligence (AI) and/or machine learning (ML) algorithm. Insome embodiments, one or more of the processes described herein can beperformed within minutes in a reproducible manner. This is starkcontrast to existing measures today which do not produce reproducibleprognosis or assessment, take extensive amounts of time, and/or requireinvasive procedures.

As such, in some embodiments, the systems, devices, and methodsdescribed herein are able to provide physicians and/or patients specificquantified and/or measured data relating to a patient's plaque that donot exist today. For example, in some embodiments, the system canprovide a specific numerical value for the volume of stable and/orunstable plaque, the ratio thereof against the total vessel volume,percentage of stenosis, and/or the like, using for example radiodensityvalues of pixels and/or regions within a medical image. In someembodiments, such detailed level of quantified plaque parameters fromimage processing and downstream analytical results can provide moreaccurate and useful tools for assessing the health and/or risk ofpatients in completely novel ways.

Disclosed herein are systems, methods, and devices for cardiovascularrisk and/or disease state assessment using image-based analyses. Inparticular, in some embodiments, the systems, devices, and methods arerelated to cardiovascular risk and/or disease and/or state assessmentusing normalized image analysis-based plaque parameters. In someembodiments, assessment of cardiovascular risk and/or disease and/orstate generated using the systems, methods, and devices herein can beutilized to diagnose and/or generate a proposed treatment for a patient.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to analyze one or more non-invasivelyobtained medical images of a subject, such as a CT image, to determineone or more plaque parameters and/or vessel parameters. For example, insome embodiments, the one or more plaque parameters can be associatedwith one or more regions of plaque and can include one or more ofpercent atheroma volume (PAV), plaque volume, total plaque volume,volume of non-calcified plaque, volume of calcified plaque, volume oflow-attenuated, non-calcified plaque, location, geometry, and/or thelike. In some embodiments, the one or more vessel parameters can includeone or more of vessel wall volume, curvature, and/or the like. Inparticular, plaque volume can be a helpful indicator of the diseasestate of a subject. However, absolute plaque volume may have its limitsfor a number of reasons. For example, absolute total plaque volume doesnot take into account the composition of plaque, such as for examplecalcified versus non-calcified and/or low-attenuated plaque. Also, evenif plaque composition is taken into account, there may be variation inplaque volume between subjects due to differences in physical propertiesof the subjects that are not necessarily related to their disease state.As such, in some embodiments, plaque volume can be normalized foranalysis to account for such shortcomings associated with analyzingabsolute plaque volume. In particular, in some embodiments, plaquevolume can be normalized to the vessel volume using plaque parameters,such as, for example, PAV. PAV can refer to the proportion of totalvessel wall volume or total vessel volume occupied by atheroscleroticplaque. In some examples, PAV can be determined on a per vessel basis.As such, in some embodiments, analyzing coronary PAV can provide anindication of risk of cardiovascular disease or a major adversecardiovascular event (MACE), such as a myocardial infarction or heartattack. In some embodiments, one or more medical images obtained from acoronary computed tomography angiography (CCTA) can be used as anon-invasive measure to assess PAV.

However, in some embodiments, analyzing PAV can also have certainlimitations. For example, for certain scanners and/or scan parameters,the quality of an image obtained from a CT scan or CCTA can be less thanperfect. For example, in some embodiments, small vessels below a certainsize can be difficult to analyze, thereby potentially resulting in lessthan accurate analysis of plaque or PAV within such vessel. In order toaddress such technical shortcomings, some embodiments of the systems,devices, and methods described herein are configured to utilize anormalized PAV in analyzing a CT or CCTA image. In particular, in someembodiments, the system can be configured to analyze a CT or CCTA imageto identify one or more vessels above a certain threshold level (e.g., athreshold vessel diameter or other thresholds as discussed below) andanalyze such vessels to determine PAV. By utilizing such a modifiedand/or normalized PAV measurement, it can be possible to address imagequality issues arising from CT or CCTA scans, as a modified and/ornormalized database of PAV values derived using the same vesselthreshold can be used as a reference database.

In some embodiments, the systems, devices, and methods can be configuredto apply such vessel thresholds prior to analyzing an image for anyparameter. For example, parameters can include any plaque parameter,vessel parameter, and/or relational parameter between the two, includingbut not limited to PAV. In some embodiments, after applying a vesselthreshold, any such parameter modified by vessel thresholds can benormalized, for example, against a physical property of the subject. Forexample, in some embodiments, the vessel threshold can comprise adiameter of about 2.0 mm, such that the system is configured to ignoreany vessels with a diameter below 2.0 mm and analyze only vessel areaswith a diameter above 2.0 mm to determine any such parameters. Othervessel thresholds can be used as described below. In some embodiments,parameters derived from vessel areas above the vessel threshold can thenbe normalized against some property of the subject and analyzed bycomparison to a database of known parameter values obtained from vesselareas above the same vessel threshold from a population with varyingdegrees of plaque and/or disease, thereby normalizing the analysis to beindependent of the image quality.

In some embodiments, the vessel threshold can be based on volume,diameter, surface area, radius, width, and/or any other variable orparameter related to the vessel. In some embodiments, the vesselthreshold can comprise a vessel diameter, radius, or width of about 10.0mm, about 9.0 mm, about 8.0 mm, about 7.0 mm, about 6.0 mm, about 5.0mm, about 4.5 mm, about 4.0 mm, about 3.9 mm, about 3.8 mm, about 3.7mm, about 3.6 mm, about 3.5 mm, about 3.4 mm, about 3.3 mm, about 3.2mm, about 3.1 mm, about 3.0 mm, about 2.9 mm, about 2.8 mm, about 2.7mm, about 2.6 mm, about 2.5 mm, about 2.4 mm, about 2.3 mm, about 2.2mm, about 2.1 mm, about 2.0 mm, about 1.9 mm, about 1.8 mm, about 1.7mm, about 1.6 mm, about 1.5 mm, about 1.4 mm, about 1.3 mm, about 1.2mm, about 1.1 mm, about 1.0 mm, about 0.9 mm, about 0.8 mm, about 0.7mm, about 0.6 mm, about 0.5 mm, about 0.4 mm, about 0.5 mm, about 0.4mm, about 0.3 mm, about 0.2 mm, about 0.1 mm, and/or within a rangedefined by two of the aforementioned values. These values can refer, forexample, to average diameters, widths, or radii along a vessel. Vesselsurface area can be the square of any of these listed values and/orother values both larger and smaller.

In some embodiments, the system can be configured to utilize a differentvessel threshold for different vessels on an image. For example, in somecases, an image can comprise vessels with varying degrees of quality,such that one vessel has a higher image quality than another vesselwithin the same image due to motion artifact or other reasons. Thesystem can be configured to perform an image analysis to determine imagequalities for vessels identified therein, for example, using machinelearning or artificial intelligence algorithms and/or other imageprocessing techniques. In some embodiments, the system can be configuredto apply a higher vessel threshold to a vessel with lower image qualitythan another vessel with a higher image quality. Similarly, in someembodiments, the system can be configured to apply a lower vesselthreshold to a vessel with a higher image quality than another vesselwith a lower image quality. In some embodiments, one or more plaque,vessel, and/or relational parameters can be derived from an image afterapplying different vessel thresholds to different vessels. For example,in some instances, a PAV for one vessel can be derived after removingall vessel areas with a diameter smaller than about 2.0 mm, whereas aPAV for another vessel within the same image can be derived afterremoving all vessel areas with a diameter smaller than about 1.0 mm.

In some embodiments, the reference database can comprise PAV or otherplaque, vessel, and/or relational parameters derived from images afterapplying varying vessel thresholds to the same and/or different vessels.For example, in some embodiments, the reference database can includePAVs derived from one vessel after removing all vessel areas with adiameter smaller than about 2.0 mm, about 1.0 mm, and/or any othervessel threshold as described herein. Then, in some embodiments, if acorresponding vessel in the subject image at hand is applied to a vesselthreshold of 1.0 mm, then that vessel segment or a parameter derivedtherefrom can be compared to corresponding vessel segments or parametersderived therefrom in the database after applying a vessel threshold of1.0 mm (with or without normalizing to a physical property of thesubject). Similarly, another vessel in the subject image, or a parameterderived therefrom, can be compared to corresponding vessel segments, orparameters derived therefrom, in the database after applying a vesselthreshold of 2.0 mm (with or without normalizing to a physical propertyof the subject). In other words, different vessel thresholds can beapplied to different vessels, and the resulting vessel segments can benormalized and/or compared to vessel segments with different appliedvessel thresholds in the reference database. As such, in someembodiments, the reference database can include modified and/ornormalized parameters for different vessels. Accordingly, in someembodiments, the system can be configured to compare each vesselsegment, normalized or not, to the same vessel segment with the sameapplied vessel threshold of different subjects. Thus, in someembodiments, the systems, methods, and devices can be configured toprovide dynamic normalization of vessels for improved accuracy and/oranalysis independent of image quality.

In some embodiments, with or without applying a vessel threshold, one ormore vessel segments can be analyzed to determine one or more plaqueparameters, vessel parameters, and/or relational plaque parameters. Forexample, the plaque parameters can include absolute plaque density,relative plaque density, composition, calcification, radiodensity,location, volume, surface area, geometry, heterogeneity, diffusivity,and/or ratio between volume and surface area, among others. The vesselparameters can include vessel volume, diameter, area, cross-sectionalarea, surface area, length, location, and/or remodeling, among others.The relational plaque parameters can include a ratio or other comparisonbetween one or more plaque parameters and one or more vessel parameters,such as for example PAV, PAV on a vessel-by-vessel basis, PAV on asegment-by-segment basis, and/or PAV for the whole heart, ratio ofsurface area of plaque to surface of vessel or lumen, ratio of volume ofplaque to volume of vessel or lumen, and/or the like.

As an illustrative example, FIG. 14A provides a schematic example of oneor more regions of plaque that can be analyzed using image analysisprocesses by one or more embodiments of the systems, methods, anddevices herein for assessment of cardiovascular risk, disease, and/orstate. As illustrated in FIG. 14A, in some embodiments, the systems,methods, and devices described herein can be configured to analyze oneor more vessels 1400 on a medical image. The one or more vessels 1400can taper along a longitudinal axis such that the diameter of the vesselgenerally decreases. As described herein, in some embodiments, thequality of the medical image can be such that certain vessel features,such as plaque 1406, in a small or narrow vessel segment can appear lessthan accurate for analysis purposes. In contrast, certain vesselfeatures, such as some other regions of plaque 1401, 14024, within thesame vessel 1400 can have sufficient image quality for analysispurposes. For example, in some embodiments, a region of plaque 1401appearing in a sufficiently wide vessel segment can be further analyzedby the system to identify and/or determine one or more regions and/ortypes and/or compositions of plaque 1403, 1404, 1405 within the regionof plaque 1401. In some embodiments, a region of plaque 1402 cancomprise a single type or composition of plaque. In some embodiments, aregion of plaque 1401, 1402, 1403, 1404, 1405 can comprise one or moredifferent types or compositions of plaque. For example, in someembodiments, the system can be configured to analyze, characterize,and/or classify plaque into one of three types: low-densitynon-calcified plaque, non-calcified plaque, and calcified plaque. Insome embodiments, the system can be configured to analyze, characterize,and/or classify plaque into one of two types: non-calcified plaque andcalcified plaque. In some embodiments, the system can be configured toanalyze, characterize, and/or classify plaque into one of any number ofdifferent types, such as for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15,20, 25, and/or 30 different types of classifications of plaque. In someembodiments, the system can be configured to analyze, characterize,and/or classify plaque into a number of different types within a rangedefined by two of the aforementioned values.

In some embodiments, due to the fact that certain vessel features, suchas a region of plaque 1406, within a narrow vessel segment comprise lowimage quality for analysis, the system can be configured to ignore orremove such features. More specifically, as described herein, in someembodiments, the system can be configured to ignore vessel segmentsbelow a certain vessel threshold 1407, the vessel threshold 1407associated with the size of the vessel segment, and/or features thereinfrom further analysis. As such, in the illustrated example, in someembodiments, the system can be configured to only analyze some of theregions of plaque 1401, 1402 that appear in a sufficiently wide vesselsegment which is above a vessel threshold 1407 and not other regions ofplaque 1406 that are below the vessel threshold 1407 within a singlevessel 1400. In some embodiments, the system can be further configuredto analyze only those regions of plaque 1401, 1402 within the portion ofthe vessel above the vessel threshold 1407 in determining one or moreplaque, vessel, and/or relational plaque parameters, such as PAV. Insome embodiments, the database PAV modified by the vessel threshold orother database parameter can be normalized, for example against the bodymass and/or LV mass and/or heart mass of the subject, and/or compared toa normal values and/or reference values database comprising modifiedand/or normalized PAV or other parameter values. Based on such analysis,in some embodiments, the system can be configured to determine coronaryartery disease (CAD) risk assessment and/or proposed treatment for asubject.

Further, even if PAV, plaque volume, or any other plaque parameter isanalyzed either with or without truncating a vessel based on athreshold, there may still be other factors that limit the usefulnessand/or indicative nature of such plaque parameter. As an illustrativeexample, FIGS. 14B-14C are schematics illustrating examples of one ormore regions of plaque that can be analyzed using image analysisprocesses by one or more embodiments of the systems, methods, anddevices herein for assessment of cardiovascular risk, disease, and/orstate. As illustrated in FIG. 14B, in some instances, a vessel 1408 caninclude a region of plaque 1409 that is inside the vessel 1408 andthereby obstructs or decreases the vessel volume. In contrast, asillustrated in FIG. 14C, in some instances, a vessel 1410 can include aregion of plaque 1411 that is outside the vessel 1410 and thereby doesnot obstruct or decrease the vessel volume. As such, in both instancesillustrated in FIGS. 14B and 14C, even if the plaque volume is the sameand even if the plaque composition is the same between the two regionsof plaque 1409, 1411, the PAV will be different due to differences invessel volume of the vessels 1408, 1410. More specifically, the PAV ofthe region of plaque 1409 inside the vessel 1408 will be higher than thePAV of plaque 1411 outside of the vessel 1410. However, if the volumeand composition of the regions of plaque 1409, 1411 are equal, then thepotential risk and/or disease state of both vessels 1408, 1410 due tothe two regions of plaque 1409, 1411 may be the same. For example, therisk of plaque 1411 rupturing may be the same as the risk of plaque 1409rupturing. However, PAV values of both regions of plaque 1409, 1411 mayindicate otherwise.

As such, to account for such technical shortcomings, in some embodimentsdescribed herein, the systems, methods, and devices are configured tonormalize and/or modify PAV for more accurate analysis of disease state.For example, in some embodiments, the systems, methods, and devicesdescribed herein are configured to generate a hypothetical PAV, whereinplaque volume is normalized against a hypothetical vessel volume thatwould have been true had the region(s) of plaque not been present. Inother words, in some embodiments, the systems, methods, and devices areconfigured to interpolate a curvature of the vessel wall before andafter where a region(s) of plaque exists and determine the hypotheticalvessel volume based on the same. For example, in the situationillustrated in FIG. 14B, because the plaque 1409 is wholly within thevessel 1408, the system can simply use the exterior vessel wall as abasis for determining the hypothetical vessel volume. However, in thesituation illustrated in FIG. 14C, the system can be configured to usethe boundaries of where the plaque 1411 exists as a basis forinterpolating the vessel wall curvature to determine a hypotheticalvessel wall 1412 had the plaque 1411 not existed. In some embodiments,based on the hypothetical vessel wall 1412, the system can be configuredto determine the hypothetical vessel volume. In some embodiments, thesystem can be configured divide the plaque volume by the hypotheticalvessel volume to determine a normalized plaque volume, which can accountfor differences in actual vessel volume, for example due to positiveremodeling.

In some embodiments, the system can be configured to determine ahypothetical PAV for one or more regions of plaque within one or morearteries or vessels identified on a medical image. For example, in someembodiments, the system can be configured to determine a hypotheticalPAV for every region of plaque that is present on a medical image and/orvessel and/or artery. In some embodiments, the system can be configuredto aggregate all such hypothetical PAV values and compare the same to areference database of known aggregate hypothetical PAV values todetermine the state and/or risk of disease. In some embodiments, thesystem can be configured to determine a hypothetical PAV for one or moreregions of plaque within a particular artery or vessel and compare thesame to a reference database of known hypothetical PAV values for thatsame artery or vessel to determine a state and/or risk of disease forthat artery or vessel. In some embodiments, the system can be configuredto repeat the process for one or more arteries or vessels and thenaggregate the same to determine an overall state and/or risk of diseasefor the subject.

In some embodiments, the systems, devices, and methods described hereincan be configured to normalize plaque volume against a physical propertyof the subject, such as, for example, left ventricular (LV) mass, toaddress shortcomings with using plaque volume directly as a proxy forthe state or risk of disease. LV mass may refer to the weight of theleft ventricle and can be used to determine blood pressure effects onthe heart. In general, the higher the LV mass, the more likely therecould be an occurrence of a cardiac incident. In some embodiments, thesystem can be configured to normalize plaque volume, such as totalplaque volume, total non-calcified plaque volume, total calcified plaquevolume, and/or total low-attenuated non-calcified plaque volume, againstLV mass. In some embodiments, LV mass can be derived from a medicalimage using image processing techniques. In some embodiments, the systemcan be configured to compare plaque volume normalized to LV mass to areference database comprising values of plaque volume normalized to LVmass derived from a population with varying degrees of health or diseaseto assess the risk and/or state of disease of the subject.

In some embodiments, the systems, devices, and methods described hereincan be configured to normalize plaque volume appearing in a particularvessel or artery against LV mass subtended by that artery or vessel inwhich the plaque appears. As such, in some embodiments, plaque can benormalized against subtended LV mass on a vessel-by-vessel basis. Asdescribed above, in some embodiments, the system can be configured tonormalize plaque volume, such as total plaque volume, totalnon-calcified plaque volume, total calcified plaque volume, and/or totallow-attenuated non-calcified plaque volume, against LV mass subtended bythe artery or vessel in which that plaque appears. In some embodiments,the LV mass and/or LV mass subtended by a particular artery can bederived from a medical image using image processing techniques. In someembodiments, the system can be configured to compare plaque volumenormalized to LV mass subtended by a vessel in which the plaque appearsto a reference database comprising values of plaque volume normalized toLV mass subtended by the vessel in which the plaque appears that can bederived from a population with varying degrees of health or disease toassess the risk and/or state of disease of the subject.

As such, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to utilize normalized PAV and/orplaque volume in assessing the risk of coronary artery disease (CAD)and/or MACE for a subject and/or determine a proposed treatment. Byutilizing such normalized or modified PAV or plaque volume, it can bepossible to address predictability and/or comparability issues arisingfrom image quality issues, positive/negative remodeling, and/ordifferences arising from physical differences in subjects, and/or thelike, by comparing the normalized and/or modified PAV or plaque volumeto a reference database of values of normalized and/or modified PAV orplaque volumes derived from a reference population using similar PAVand/or plaque normalization and/or modification techniques.

In some embodiments, the medical image of the subject and/or referencedatabase is obtained using an imaging technique comprising one or moreof computed tomography (CT), x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), magnetic resonance (MR) imaging,optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS). Forexample, in some embodiments, images for the reference database can bederived from one or more or a combination of such imaging modalities forvarying degrees of image quality.

In some embodiments, the system can be configured to assess the riskand/or state of cardiovascular disease or health based on the modifiedand/or normalized plaque parameter(s). In some embodiments, the systemcan be configured to determine or generate a proposed treatment for thesubject based on the assessed risk and/or state of cardiovasculardisease or health. For example, the proposed treatment can include oneor more of medical therapy (such as statins), interventional therapy(such as stent implantation), and/or lifestyle therapy (such as diet orexercise). In some embodiments, the system can be configured to trackthe efficacy of a treatment by tracking changes in the modified and/ornormalized parameter(s), for example compared to previous value(s) forthe same subject and/or change relative to a reference values databasecomprising one or more reference values, such as, for example, normalvalues.

As such, in some embodiments, the systems, devices, and methodsdescribed herein provide an improved quantitative and/or image-basedsolution for generating and/or tracking cardiovascular disease or healthby modifying and/or normalizing one or more plaque parameters, such asfor example PAV, plaque volume, and/or the like.

Cardiovascular Risk and/or Disease State Assessment Using Modifiedand/or Normalized Image Analysis-Based Plaque Parameters

As described herein, some embodiments of systems, devices, and methodsdescribed herein are configured to derive one or more modified and/ornormalized plaque parameters from a medical image and use the same forrisk assessment and/or treatment assessment for CAD. In someembodiments, such modified and/or normalized plaque parameters can beanalyzed against a reference database in determining the risk and/ortreatment assessment. FIGS. 14D-14G are flowcharts illustrating exampleembodiment(s) of systems, devices, and methods for cardiovascular riskand/or disease state assessment using modified and/or normalized imageanalysis-based plaque parameters.

As illustrated in FIG. 14D, in some embodiments, the system can beconfigured to normalize plaque volume against a hypothetical vesselvolume without plaque to account for differences in vessel remodelingdue to plaque. As such, in some embodiments, the systems, methods, anddevices described herein can provide a more accurate benchmark forcomparison and/or analysis among different subjects.

In particular, as illustrated in FIG. 14D, in some embodiments, thesystem can be configured to access a medical image at block 1413. Insome embodiments, the medical image can include one or more arteries,such as coronary, carotid, and/or other arteries of a subject. In someembodiments, the medical image can be stored in a medical image database1414. In some embodiments, the medical image database 1414 can belocally accessible by the system and/or can be located remotely andaccessible through a network connection. The medical image can comprisean image obtain using one or more modalities such as for example, CT,Dual-Energy Computed Tomography (DECT), Spectral CT, photon-counting CT,x-ray, ultrasound, echocardiography, intravascular ultrasound (IVUS),Magnetic Resonance (MR) imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS). In some embodiments, the medical image comprisesone or more of a contrast-enhanced CT image, non-contrast CT image, MRimage, and/or an image obtained using any of the modalities describedabove.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 1415, thesystem can be configured to identify and/or characterize one or morevessels and/or lumen of interest, such as of one or more arteries. Theone or more arteries can include coronary arteries, carotid arteries,aorta, renal artery, lower extremity artery, upper extremity artery,and/or cerebral artery, amongst others. In some embodiments, the systemcan be configured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically identify one or more arteries orcoronary arteries using image processing. For example, in someembodiments, the one or more AI and/or ML algorithms can be trainedusing a Convolutional Neural Network (CNN) on a set of medical images onwhich arteries or coronary arteries have been identified, therebyallowing the AI and/or ML algorithm automatically identify arteries orcoronary arteries directly from a medical image. In some embodiments,the arteries or coronary arteries are identified by size and/orlocation.

In some embodiments, at block 1416, the system can be configured toidentify one or more regions of plaque within the medical image. In someembodiments, at block 1417, the system can be configured to analyzeand/or determine one or more plaque and/or vessel parameters. In someembodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically identify and/orcharacterize one or more regions of plaque using image processing. Forexample, in some embodiments, the one or more AI and/or ML algorithmscan be trained using a Convolutional Neural Network (CNN) on a set ofmedical images on which one or more regions of plaque have beenidentified and/or characterized, thereby allowing the AI and/or MLalgorithm to automatically identify and/or characterize regions ofplaque directly from a medical image.

For example, in some embodiments, the system can be configured toanalyze the absolute density, relative density, radiodensity values,and/or heterogeneity or distribution thereof of one or more pixels on amedical image to identify and/or characterize plaque. More specifically,in some embodiments, the system can be configured to identify aparticular pixel as plaque, non-calcified plaque, low-densitynon-calcified plaque, and/or calcified plaque, and/or any otherclassification or type of plaque. In some embodiments, the system can beconfigured to classify a particular pixel or region of plaque asnon-calcified plaque when the radiodensity value of the pixel is below acertain predetermined threshold, within a predetermined range, and/orcomprises a heterogeneity index or distribution within, under, or abovea particular predetermined range or threshold. In some embodiments, thesystem can be configured to classify a particular pixel or region ofplaque as low-density non-calcified plaque when the radiodensity valueof the pixel is below a certain predetermined threshold, within apredetermined range, and/or comprises a heterogeneity index ordistribution within, under, or above a particular predetermined range orthreshold. In some embodiments, the system can be configured to classifya particular pixel or region of plaque as calcified plaque when theradiodensity value of the pixel is above a certain predeterminedthreshold, within a predetermined range, and/or comprises aheterogeneity index or distribution within, under, or above a particularpredetermined range or threshold.

In some embodiments, the one or more plaque parameters can includeabsolute plaque density, relative plaque density, composition,calcification, radiodensity, location, volume, surface area, geometry,heterogeneity, diffusivity, and/or ratio between volume and surfacearea. In some embodiments, the one or more vessel parameters can includeone or more of vascular volume, diameter, area, cross-sectional area,surface area, length, location, and/or remodeling. In some embodiments,the one or more plaque parameters can include a ratio or othercomparison between one or more plaque parameters and one or more vesselparameters, such as for example PAV, PAV on a vessel-by-vessel basis,PAV on a segment-by-segment basis, and/or PAV for the whole heart, ratioof surface area of plaque to surface of vessel or lumen, ratio of volumeof plaque to volume of vessel or lumen, and/or the like.

In some embodiments, the system can be configured to utilize one or moreAI and/or ML algorithms to automatically and/or dynamically determineone or more plaque parameters and/or vessel parameters using imageprocessing. For example, in some embodiments, the one or more AI and/orML algorithms can be trained using a Convolutional Neural Network (CNN)on a set of medical images on which one or more plaque parameters and/orvessel parameters have been determined, thereby allowing the AI and/orML algorithm to automatically determine one or more plaque parametersand/or vessel parameters directly from a medical image.

In some embodiments, at block 1418, the system can be configured togenerate a hypothetical vessel volume without plaque. In particular, insome embodiments, the system can be configured to assume that one ormore regions of plaque do not exist in an artery or vessel and determinewhat the vessel volume might have been without such one or more regionsof plaque. In some embodiments, the system can be configured todetermine the boundaries of one or more regions of plaque andinterpolate the curvature of the vessel or artery based on the startand/or end points or boundary of the one or more regions of plaque.Boundaries of the one or more regions of plaque can include a posteriorboundary and an anterior boundary opposite the posterior boundary. Theposterior boundary and anterior boundary can be found, at least in part,by using the location and geometry of one or more regions of plaque thathave been identified within the artery. For example, in someembodiments, the system can be configured to determine the geometryand/or location of a region of plaque and/or graphically remove theregion of plaque from a vessel. In some embodiments, the system can beconfigured to interpolate what the curvature of the vessel would havebeen without the removed region of plaque. In some embodiments, thesystem can be configured to utilize one or more AI and/or ML algorithmsto determine the hypothetical vessel volume and/or vessel curvature, forexample by being trained on a plurality of vessel curvatures and/orvolumes without plaque.

In some embodiments, at block 1419, the system can be configured togenerate a hypothetical PAV based on the hypothetical vessel volume. Forexample, in some embodiments, the system can be configured to generate ahypothetical PAV by dividing plaque volume by the hypothetical vesselvolume. In some embodiments, the system can be configured to generate ahypothetical PAV on a vessel-by-vessel basis. For example, in someembodiments, the system can be configured to generate a vessel-specifichypothetical PAV by determining the amount or volume of plaque (total,non-calcified, calcified, and/or low-attenuated non-calcified plaque)within a particular vessel and then dividing the same by thehypothetical vessel volume of that specific vessel. In some embodiments,the system can be configured to generate a weighted measure of one ormore vessel-specific hypothetical PAV values. For example, in someembodiments, the system can be configured to weight a hypothetical PAVof a particular vessel more heavily compared to a hypothetical PAV ofanother vessel. In some embodiments, the system can be configured togenerate a global or aggregate hypothetical PAV for the subject bydetermining the amount or volume of plaque (total, non-calcified,calcified, and/or low-attenuated non-calcified plaque) within aplurality of vessels and then dividing the same by the hypotheticalvessel volume of those plurality of vessels.

In some embodiments, at block 1420, the system can be configured toanalyze the hypothetical PAV based on one or more reference values ofhypothetical PAV. Individual reference values of hypothetical PAV may beused from a dataset of reference hypothetical PAV values derived from aplurality of medical images of a population with varying states ofcardiovascular disease. For example, in some embodiments, the system canbe configured to access a reference values database 1421 that includesone or more hypothetical PAV values. The one or more hypothetical PAVvalues can be derived from other subjects with varying states or risksof cardiovascular disease, including for example normal values. In someembodiments, the one or more reference values can be obtained from oneor more medical images using the same or similar imaging modalities asthe medical image accessed at block 1413. In some embodiments, the oneor more reference values can be obtained from analyzing one or moremedical images to derive one or more hypothetical PAV values, forexample using one or more processes described in relation to blocks1413-1419. In some embodiments, the one or more reference hypotheticalPAV values can be stored on a reference values database 1421, which canbe locally accessible by the system and/or can be located remotely andaccessible through a network connection.

In some embodiments, based on such analysis and/or comparison, thesystem, at block 1422, can be configured to generate a graphicalrepresentation of the analysis results. In some embodiments, the systemcan be configured to generate a graphical generation reporting theanalysis results on a vessel-by-vessel basis and/or subject basis. Forexample, in some embodiments, the system can be configured to generate agraphical representation of one or more arteries or vessels, in whichspecific arteries and/or vessels can be color-coded or assigned somevalue or other indicator depending on the analysis results. As anillustrative example, in some embodiments, if the system determines thatthe hypothetical PAV of a particular vessel is high, the system can beconfigured to color code that vessel red in the graphicalrepresentation.

In some embodiments, based on such analysis and/or comparison ofhypothetical PAV, the system, at block 1423, can be configured todetermine a risk or state of cardiovascular disease or health of thesubject. Further, in some embodiments, based on such analysis and/orcomparison, the system, at block 1423, can be configured to determine aproposed treatment for the subject. The treatment can include, forexample, medical treatment such as statins, interventional treatmentsuch as stent implantation, and/or lifestyle treatment such as exerciseor diet. In some embodiments, in determining the risk or state ofcardiovascular disease or health and/or treatment, the system can accessa plaque risk/treatment database 1424, which can be locally accessibleby the system and/or can be located remotely and accessible through anetwork connection. In some embodiments, the plaque risk/treatmentdatabase 1424 can include reference points or data that relate one ormore treatment to cardiovascular disease risk or state determined basedon one or more reference hypothetical PAV values.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1413-1423, for example for oneor more other vessels, segment, regions of plaque, different subjects,and/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease trackingand/or personalized treatment for a subject. In some embodiments, thesystem can track the efficacy of the treatment by determining assessmentof the state of cardiovascular disease of the subject at a later pointin time after the treatment.

FIG. 14E is a flowchart illustrating example embodiment(s) of systems,devices, and methods for cardiovascular risk and/or disease stateassessment using modified and/or normalized image analysis-based plaqueparameters. The same reference numbers in FIGS. 14E and 14D representsimilar features and can include any of the features described inreference to either figure.

As illustrated in FIG. 14E, in some embodiments, the system can beconfigured to normalize plaque volume against LV mass to account forother differences among subjects that may not necessarily be indicativeof disease state or risk. As such, in some embodiments, the systems,methods, and devices described herein can provide a more accuratebenchmark for comparison and/or analysis among different subjects.

In particular, as illustrated in FIG. 14E, in some embodiments, thesystem, at block 1425, can be configured to determine LV mass of thesubject. For example, in some embodiments, the system can be configuredto derive LV mass from the medical image using one or more imageanalysis techniques. In particular, in some embodiments, the system canbe configured to derive LV volume based on the medical image andconverting the same to LV mass. In some embodiments, the system can beconfigured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically determine LV mass using imageprocessing. For example, in some embodiments, the one or more AI and/orML algorithms can be trained using a Convolutional Neural Network (CNN)on a set of medical images on which LV mass and/or volume have beenderived, thereby allowing the AI and/or ML algorithm automaticallyderive LV mass and/or volume directly from a medical image.

In some embodiments, at block 1426, the system can be configured tonormalize plaque volume by dividing the plaque volume by LV mass. Insome embodiments, the system can be configured to divide one or more ofthe total plaque volume, total non-calcified plaque volume, totalcalcified plaque volume, and/or total low-attenuated, non-calcifiedplaque volume by LV mass.

In some embodiments, at block 1427, the system can be configured toanalyze the normalized plaque volume based on one or more referencevalues of normalized plaque volume. For example, in some embodiments,the system can be configured to access a reference values database 1428that includes one or more values of normalized plaque volume. The one ormore values of normalized plaque volume can be derived from othersubjects with varying states or risks of cardiovascular disease,including for example normal values. In some embodiments, the one ormore reference values can be obtained from one or more medical imagesusing the same or similar imaging modalities as the medical imageaccessed at block 1413. In some embodiments, the one or more referencevalues can be obtained from analyzing one or more medical images toderive one or more values of normalized plaque volume, for example usingone or more processes described in relation to blocks 1413-1426. In someembodiments, the one or more reference values of normalized plaquevolume can be stored on a reference values database 1428, which can belocally accessible by the system and/or can be located remotely andaccessible through a network connection.

In some embodiments, based on such analysis and/or comparison, thesystem, at block 1429, can be configured to generate a graphicalrepresentation of the analysis results. In some embodiments, the systemcan be configured to generate a graphical generation reporting theanalysis results on a subject basis.

In some embodiments, based on such analysis and/or comparison ofnormalized plaque volume, the system, at block 1430, can be configuredto determine a risk or state of cardiovascular disease or health of thesubject. Further, in some embodiments, based on such analysis and/orcomparison, the system, at block 1430, can be configured to determine aproposed treatment for the subject. The treatment can include, forexample, medical treatment such as statins, interventional treatmentsuch as stent implantation, and/or lifestyle treatment such as exerciseor diet. In some embodiments, in determining the risk or state ofcardiovascular disease or health and/or treatment, the system can accessa plaque risk/treatment database 1431, which can be locally accessibleby the system and/or can be located remotely and accessible through anetwork connection. In some embodiments, the plaque risk/treatmentdatabase 1431 can include reference points or data that relate one ormore treatment to cardiovascular disease risk or state determined basedon one or more reference values of plaque volume normalized to LV mass.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1413-1430, for example for oneor more different subjects and/or for the same subject at a differenttime. As such, in some embodiments, the system can provide forlongitudinal disease tracking and/or personalized treatment for asubject.

FIG. 14F is a flowchart illustrating example embodiment(s) of systems,devices, and methods for cardiovascular risk and/or disease stateassessment using modified and/or normalized image analysis-based plaqueparameters. The same reference numbers in FIGS. 14F, 14E, and 14Drepresent similar features and can include any of the features describedin reference to any of these figures.

As illustrated in FIG. 14F, in some embodiments, the system can beconfigured to normalize plaque volume within a vessel against the amountof myocardium that vessel subtends to account for other differencesamong subjects that may not necessarily be indicative of disease stateor risk. As such, in some embodiments, the systems, methods, and devicesdescribed herein can provide a more accurate benchmark for comparisonand/or analysis among different subjects.

In particular, as illustrated in FIG. 14F, in some embodiments, thesystem, at block 1432, can be configured to determine myocardium or LVmass of the subject subtended by one or more vessels. For example, insome embodiments, the system can be configured to determine LV masssubtended by each or some of the vessels identified from the medicalimage at block 1415. In some embodiments, the system can be configuredto derive LV mass subtended by one or more vessels from the medicalimage using one or more image analysis techniques. In particular, insome embodiments, the system can be configured to derive LV volumesubtended by one or more vessels based on the medical image andconverting the same to LV mass. In some embodiments, the system can beconfigured to utilize one or more AI and/or ML algorithms toautomatically and/or dynamically determine LV mass subtended by one ormore vessels using image processing. For example, in some embodiments,the one or more AI and/or ML algorithms can be trained using aConvolutional Neural Network (CNN) on a set of medical images on whichLV mass and/or volume subtended by one or more vessels have beenderived, thereby allowing the AI and/or ML algorithm automaticallyderive LV mass and/or volume subtended by one or more vessels directlyfrom a medical image.

In some embodiments, at block 1433, the system can be configured tonormalize the volume of plaque in a particular vessel by dividing thesame by LV mass subtended by that particular vessel. In someembodiments, the system can be configured to divide one or more of thetotal plaque volume, total non-calcified plaque volume, total calcifiedplaque volume, and/or total low-attenuated, non-calcified plaque volumeof one or more regions of plaque within a particular vessel by LV masssubtended by that vessel.

As described herein, in some embodiments, the system can be configuredto normalize plaque volume on a vessel-by-vessel basis. In someembodiments, the system can be configured to generate a weighted measureof one or more plaque volumes normalized against LV mass subtended byeach vessel. For example, in some embodiments, the system can beconfigured to weight a normalized volume of plaque within a particularvessel more heavily compared to a normalized volume of plaque withinanother vessel. In some embodiments, the system can be configured togenerate a global or aggregate normalized plaque volume for the subjectby adding and/or determining a weighted measure of a plurality of plaquevolumes normalized by the LV mass subtended by the vessel in which theregions of plaque exist.

In some embodiments, at block 1434, the system can be configured toanalyze the normalized plaque volume(s) based on one or more referencevalues of volumes of plaque normalized against LV mass subtended by avessel comprising each volume or region of plaque. For example, in someembodiments, the system can be configured to access a reference valuesdatabase 1435 that includes one or more plaque volumes normalizedagainst LV mass subtended by a vessel in which the plaque exists. Theone or more normalized plaque volumes can be derived from other subjectswith varying states or risks of cardiovascular disease, including forexample normal values. In some embodiments, the one or more referencevalues can be obtained from one or more medical images using the same orsimilar imaging modalities as the medical image accessed at block 1413.In some embodiments, the one or more reference values can be obtainedfrom analyzing one or more medical images to derive one or morenormalized plaque volumes, for example using one or more processesdescribed in relation to blocks 1413-1433. In some embodiments, the oneor more reference normalized plaque volumes can be stored on a referencevalues database 1435, which can be locally accessible by the systemand/or can be located remotely and accessible through a networkconnection.

In some embodiments, based on such analysis and/or comparison, thesystem, at block 1436, can be configured to generate a graphicalrepresentation of the analysis results. In some embodiments, the systemcan be configured to generate a graphical generation reporting theanalysis results on a vessel-by-vessel basis and/or subject basis. Forexample, in some embodiments, the system can be configured to generate agraphical representation of one or more arteries or vessels, in whichspecific arteries and/or vessels can be color-coded or assigned somevalue or other indicator depending on the analysis results. As anillustrative example, in some embodiments, if the system determines thatthe normalized plaque volume of a particular vessel is high, the systemcan be configured to color code that vessel red in the graphicalrepresentation.

In some embodiments, based on such analysis and/or comparison ofnormalized plaque volume, the system, at block 1437, can be configuredto determine a risk or state of cardiovascular disease or health of thesubject. Further, in some embodiments, based on such analysis and/orcomparison, the system, at block 1437, can be configured to determine aproposed treatment for the subject. The treatment can include, forexample, medical treatment such as statins, interventional treatmentsuch as stent implantation, and/or lifestyle treatment such as exerciseor diet. In some embodiments, in determining the risk or state ofcardiovascular disease or health and/or treatment, the system can accessa plaque risk/treatment database 1438, which can be locally accessibleby the system and/or can be located remotely and accessible through anetwork connection. In some embodiments, the plaque risk/treatmentdatabase 1438 can include reference points or data that relate one ormore treatment to cardiovascular disease risk or state determined basedon one or more reference values of plaque volumes normalized against LVmass subtended by a vessel in which the plaque appears.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1413-1438, for example for oneor more other vessels, segment, regions of plaque, different subjects,and/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease trackingand/or personalized treatment for a subject.

FIG. 14G is a flowchart illustrating example embodiment(s) of systems,devices, and methods for cardiovascular risk and/or disease stateassessment using modified and/or normalized image analysis-based plaqueparameters. The same reference numbers in FIGS. 14G, 14F, 14E, and 14Drepresent similar features and can include any of the features describedin reference to any of these figures.

As illustrated in FIG. 14G, in some embodiments, the system can beconfigured to normalize plaque volume using a plurality of methods. Insome embodiments, the system can be configured to generate a weightedmeasure of plaque volume normalized using a plurality of methods and usethe weighted measure in assessing the risk and/or state of disease forthe subject. As such, by utilizing a number of different normalizationmethods at once, in some embodiments, the systems, methods, and devicesdescribed herein can provide a more accurate benchmark for comparisonand/or analysis among different subjects.

In particular, as illustrated in FIG. 14G, in some embodiments, atblocks 1418-1419, the system can be configured to normalize plaquevolume against a hypothetical vessel volume and/or generate ahypothetical PAV. In some embodiments, the system, at blocks 1425-1426,can be configured to normalize total plaque volume against LV mass. Insome embodiments, the system, at block 1432-1433, can be configured tonormalize plaque volume against LV mass subtended by the vessel in whichthe plaque appears.

In some embodiments, at block 1439, the system can be configured togenerate a weighted measure of the plaque volume normalized againstdifferent measures. In particular, in some embodiments, the system canbe configured to generate a weighted measure of plaque volume normalizedagainst a hypothetical vessel volume or hypothetical PAV, plaque volumenormalized against LV mass, and plaque volume normalized against LV masssubtended by a vessel in which the plaque appears. In some embodiments,the system can be configured weight plaque normalized by a particularmethod more heavily than others. In some embodiments, one or more plaquenormalization methods can be weighted 0 and/or 1, although otherweighting factors can be used as well.

In some embodiments, at block 1440, the system can be configured toanalyze the weighted measure of normalized plaque volumes based on oneor more reference values of weighted measures of normalized plaquevolumes. For example, in some embodiments, the system can be configuredto access a reference values database 1441 that includes one or moreweighted measures of normalized plaque volumes. The weighted measures ofnormalized plaque volumes can be derived from other subjects withvarying states or risks of cardiovascular disease, including for examplenormal values. In some embodiments, the one or more reference values canbe obtained from one or more medical images using the same or similarimaging modalities as the medical image accessed at block 1413. In someembodiments, the one or more reference values can be obtained fromanalyzing one or more medical images to derive one or more weightedmeasures of normalized plaque volumes, for example using one or moreprocesses described in relation to blocks 1413-1439. In someembodiments, the one or more reference weighted measures of normalizedplaque volumes can be stored on a reference values database 1441, whichcan be locally accessible by the system and/or can be located remotelyand accessible through a network connection.

In some embodiments, based on such analysis and/or comparison, thesystem, at block 1442, can be configured to generate a graphicalrepresentation of the analysis results. In some embodiments, the systemcan be configured to generate a graphical generation reporting theanalysis results on a vessel-by-vessel basis and/or subject basis. Forexample, in some embodiments, the system can be configured to generate agraphical representation of one or more arteries or vessels, in whichspecific arteries and/or vessels can be color-coded or assigned somevalue or other indicator depending on the analysis results. As anillustrative example, in some embodiments, if the system determines thatthe weighted normalized plaque volume of a particular vessel is high,the system can be configured to color code that vessel red in thegraphical representation.

In some embodiments, based on such analysis and/or comparison ofweighted measures of normalized plaque volumes, the system, at block1443, can be configured to determine a risk or state of cardiovasculardisease or health of the subject. Further, in some embodiments, based onsuch analysis and/or comparison, the system, at block 1443, can beconfigured to determine a proposed treatment for the subject. Thetreatment can include, for example, medical treatment such as statins,interventional treatment such as stent implantation, and/or lifestyletreatment such as exercise or diet. In some embodiments, in determiningthe risk or state of cardiovascular disease or health and/or treatment,the system can access a plaque risk/treatment database 1444, which canbe locally accessible by the system and/or can be located remotely andaccessible through a network connection. In some embodiments, the plaquerisk/treatment database 1444 can include reference points or data thatrelate one or more treatment to cardiovascular disease risk or statedetermined based on one or more reference values of weighted measures ofnormalized plaque volumes.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1413-1444, for example for oneor more other vessels, segment, regions of plaque, different subjects,and/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease trackingand/or personalized treatment for a subject.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 14H. The example computer system 1445 is incommunication with one or more computing systems 1454 and/or one or moredata sources 1455 via one or more networks 1453. While FIG. 14Hillustrates an embodiment of a computing system 1445, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1445 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1445 can comprise a Plaque Analysis and/or RiskAssessment Module 1451 that carries out the functions, methods, acts,and/or processes described herein. The Plaque Analysis and/or RiskAssessment Module 1451 executed on the computer system 1445 by a centralprocessing unit 1447 discussed further below. Other features of thecomputer system 1445 can be similar to corresponding features of thecomputer system of FIG. 9G, described above.

Certain Examples of Embodiments of Normalized Plaque Parameters

The following are non-limiting examples of certain embodiments ofsystems and methods for normalized plaque parameters. Other embodimentsmay include one or more other features, or different features, that arediscussed herein.

Embodiment 1: A computer-implemented method of assessing a state ofcardiovascular disease of a subject based on one or more normalizedplaque parameters derived from non-invasive medical image analysis, themethod comprising: accessing, by a computer system, a medical image of asubject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries; determining, by thecomputer system, one or more vessel parameters associated with theidentified one or more arteries, the one or more vessel parameterscomprising vessel wall, volume, and curvature of the one or moreidentified arteries; analyzing, by the computer system, the medicalimage of the subject to identify one or more regions of plaque withinthe one or more arteries; determining, by the computer system, one ormore plaque parameters associated with the identified one or moreregions of plaque, the one or more plaque parameters comprising plaquevolume, location, and geometry of the one or more identified regions ofplaque; determining, by the computer system, a hypothetical vesselvolume of the one or more identified arteries without the one or moreregions of plaque, wherein the hypothetical vessel volume is determinedby: identifying a posterior boundary and an anterior boundary of the oneor more regions of plaque along the vessel wall of the one or morearteries based at least in part on the location and geometry of the oneor more regions of plaque; graphically removing the one or more regionsof plaque from the one or more arteries; interpolating a hypotheticalcurvature of the one or more arteries without the one or more regions ofplaque based at least in part on the curvature of the one or moreidentified arteries and the identified posterior boundary and theanterior boundary of the one or more regions of plaque along the vesselwall of the one or more arteries after graphically removing the one ormore regions of plaque; and determining the hypothetical vessel volumebased at least in part on the volume of the one or more identifiedarteries and the interpolated hypothetical curvature of the one or morearteries without the one or more regions of plaque; normalizing, by thecomputer system, percent atheroma volume (PAV) by generating ahypothetical PAV value based at least in part on the volume of the oneor more regions of plaque and the hypothetical vessel volume; analyzing,by the computer system, the hypothetical PAV value by comparison to adataset of reference hypothetical PAV values derived from a plurality ofmedical images of a population with varying states of cardiovasculardisease; and determining, by the computer system, an assessment of astate of cardiovascular disease of the subject based at least in part onanalysis of the hypothetical PAV value, wherein the computer systemcomprises a computer processor and an electronic storage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinthe hypothetical PAV value is determined on a vessel-by-vessel basis.

Embodiment 3: The computer-implemented method of Embodiment 2, furthercomprising: determining, by the computer system, hypothetical PAV valuesfor a plurality of vessels; analyzing, by the computer system, thehypothetical PAV value for each of the plurality of vessels bycomparison to the dataset of reference hypothetical PAV values; anddetermining, by the computer system, the assessment of the state ofcardiovascular disease of the subject based at least in part on analysisof the hypothetical PAV value for each of the plurality of vessels.

Embodiment 4: The computer-implemented method of Embodiment 2, furthercomprising: determining, by the computer system, hypothetical PAV valuesfor a plurality of vessels; generating, by the computer system, aweighted measure of the hypothetical PAV values for the plurality ofvessels; and determining, by the computer system, the assessment of thestate of cardiovascular disease of the subject based at least in part onthe weighted measure of the hypothetical PAV values for the plurality ofvessels.

Embodiment 5: The computer-implemented method of Embodiment 1, furthercomprising generating, by the computer system, a treatment forcardiovascular disease for the subject based at least in part on thedetermined assessment of the state of cardiovascular disease.

Embodiment 6: The computer-implemented method of Embodiment 5, whereinthe treatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.

Embodiment 7: The computer-implemented method of Embodiment 5, furthercomprising tracking, by the computer system, efficacy of the treatmentby determining assessment of the state of cardiovascular disease of thesubject at a later point in time after treatment.

Embodiment 8: The computer-implemented method of Embodiment 1, whereinthe hypothetical PAV value is generated based at least in part on atotal volume of the one or more regions of plaque and the hypotheticalvessel volume.

Embodiment 9: The computer-implemented method of Embodiment 1, whereinthe hypothetical PAV value is generated based at least in part on volumeof non-calcified plaque of the one or more regions of plaque and thehypothetical vessel volume.

Embodiment 10: The computer-implemented method of Embodiment 1, whereinthe hypothetical PAV value is generated based at least in part on volumeof low density, non-calcified plaque of the one or more regions ofplaque and the hypothetical vessel volume.

Embodiment 11: The computer-implemented method of Embodiment 1, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), magnetic resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).

Embodiment 12: A computer-implemented method of assessing a state ofcardiovascular disease of a subject based on one or more normalizedplaque parameters derived from non-invasive medical image analysis, themethod comprising: accessing, by a computer system, a medical image of asubject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries; analyzing, by the computersystem, the medical image of the subject to identify one or more regionsof plaque within the one or more arteries; determining, by the computersystem, one or more plaque parameters associated with the identified oneor more regions of plaque, the one or more plaque parameters comprisingplaque volume of the one or more identified regions of plaque;determining, by the computer system, left ventricular mass of thesubject; normalizing, by the computer system, the plaque volume bydividing the plaque volume of the one or more regions of plaque by thedetermined left ventricular mass of the subject; analyzing, by thecomputer system, the normalized plaque volume by comparison to a datasetof reference normalized plaque volumes, the reference normalized plaquevolumes generated by: accessing a database of a plurality of medicalimages obtained from a population with varying states of cardiovasculardisease; determining, for each of the plurality of medical images,plaque volume and left ventricular mass; normalizing, for each of theplurality of medical images, plaque volume by dividing the plaque volumeby the left ventricular mass; and generating the database of referencenormalized plaque volumes by aggregating the normalized plaque volumederived from each of the plurality of medical images; and determining,by the computer system, an assessment of a state of cardiovasculardisease of the subject based at least in part on analysis of thenormalized plaque volume, wherein the computer system comprises acomputer processor and an electronic storage medium.

Embodiment 13: The computer-implemented method of Embodiment 12, whereinthe left ventricular mass of the subject is determined based at least inpart on the medical image of the subject.

Embodiment 14: The computer-implemented method of Embodiment 12, whereinthe plaque volume is determined on a vessel-by-vessel basis.

Embodiment 15: The computer-implemented method of Embodiment 14, furthercomprising: determining, by the computer system, the plaque volume for aplurality of vessels; normalizing, by the computer system, the plaquevolume for each of the plurality of vessels by dividing he plaque volumefor each of the plurality of vessels by the determined left ventricularmass of the subject; and determining, by the computer system, theassessment of the state of cardiovascular disease of the subject basedat least in part on analysis of the normalized plaque volume for each ofthe plurality of vessels.

Embodiment 16: The computer-implemented method of Embodiment 12, furthercomprising: determining, by the computer system, the plaque volume for aplurality of vessels; generating, by the computer system, a weightedmeasure of the plaque volume for the plurality of vessels; anddetermining, by the computer system, the assessment of the state ofcardiovascular disease of the subject based at least in part on theweighted measure of the plaque volume for the plurality of vessels.

Embodiment 17: The computer-implemented method of Embodiment 12, furthercomprising generating, by the computer system, a treatment forcardiovascular disease for the subject based at least in part on thedetermined assessment of the state of cardiovascular disease.

Embodiment 18: The computer-implemented method of Embodiment 17, whereinthe treatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.

Embodiment 19: The computer-implemented method of Embodiment 17, furthercomprising tracking, by the computer system, efficacy of the treatmentby determining assessment of the state of cardiovascular disease of thesubject at a later point in time after treatment.

Embodiment 20: The computer-implemented method of Embodiment 12, whereinthe plaque volume comprises total plaque volume of the one or moreregions of plaque.

Embodiment 21: The computer-implemented method of Embodiment 12, whereinthe plaque volume comprises volume of non-calcified plaque of the one ormore regions of plaque.

Embodiment 22: The computer-implemented method of Embodiment 12, whereinthe plaque volume comprises volume of low density, non-calcified plaqueof the one or more regions of plaque.

Embodiment 23: The computer-implemented method of Embodiment 12, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), magnetic resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).

Embodiment 24: A computer-implemented method of assessing a state ofcardiovascular disease of a subject based on one or more normalizedplaque parameters derived from non-invasive medical image analysis, themethod comprising: accessing, by a computer system, a medical image of asubject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries; analyzing, by the computersystem, the medical image of the subject to identify one or more regionsof plaque within the one or more arteries; determining, by the computersystem, one or more plaque parameters associated with the identified oneor more regions of plaque, the one or more plaque parameters comprisingplaque volume of the one or more identified regions of plaque;determining, by the computer system, left ventricular mass of thesubject subtended by the one or more arteries; normalizing, by thecomputer system, the plaque volume by dividing the plaque volume of theone or more regions of plaque by the determined left ventricular mass ofthe subject subtended by the one or more arteries; analyzing, by thecomputer system, the normalized plaque volume by comparison to a datasetof reference normalized plaque volumes, the reference normalized plaquevolumes generated by: accessing a database of a plurality of medicalimages obtained from a population with varying states of cardiovasculardisease; determining, for each of the plurality of medical images,plaque volume in one or more arteries and left ventricular masssubtended by the one or more arteries; normalizing, for each of theplurality of medical images, plaque volume in the one or more arteriesby dividing the plaque volume in the one or more arteries by the leftventricular mass subtended by the one or more arteries; and generatingthe database of reference normalized plaque volumes by aggregating thenormalized plaque volume in the one or more arteries derived from eachof the plurality of medical images; and determining, by the computersystem, an assessment of a state of cardiovascular disease of thesubject based at least in part on analysis of the normalized plaquevolume, wherein the computer system comprises a computer processor andan electronic storage medium.

Embodiment 25: The computer-implemented method of Embodiment 24, whereinthe left ventricular mass of the subject subtended by the one or morearteries is determined based at least in part on the medical image ofthe subject.

Embodiment 26: The computer-implemented method of Embodiment 24, whereinthe plaque volume is determined on a vessel-by-vessel basis.

Embodiment 27: The computer-implemented method of Embodiment 26, furthercomprising: determining, by the computer system, the plaque volume for aplurality of vessels; determining, by the computer system, leftventricular mass of the subject subtended by each of the plurality ofvessels; normalizing, by the computer system, the plaque volume for eachof the plurality of vessels by dividing the plaque volume for each ofthe plurality of vessels by the determined left ventricular mass of thesubject subtended by each of the plurality of vessels; and determining,by the computer system, the assessment of the state of cardiovasculardisease of the subject based at least in part on analysis of thenormalized plaque volume for each of the plurality of vessels.

Embodiment 28: The computer-implemented method of Embodiment 24, furthercomprising: determining, by the computer system, the plaque volume for aplurality of vessels; determining, by the computer system, leftventricular mass of the subject subtended by each of the plurality ofvessels; normalizing, by the computer system, the plaque volume for eachof the plurality of vessels by dividing the plaque volume for each ofthe plurality of vessels by the determined left ventricular mass of thesubject subtended by each of the plurality of vessels; generating, bythe computer system, a weighted measure of the normalized plaque volumefor each of the plurality of vessels; and determining, by the computersystem, the assessment of the state of cardiovascular disease of thesubject based at least in part on the weighted measure of the normalizedplaque volume for each of the plurality of vessels.

Embodiment 29: The computer-implemented method of Embodiment 24, furthercomprising generating, by the computer system, a treatment forcardiovascular disease for the subject based at least in part on thedetermined assessment of the state of cardiovascular disease.

Embodiment 30: The computer-implemented method of Embodiment 29, whereinthe treatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.

Embodiment 31: The computer-implemented method of Embodiment 29, furthercomprising tracking, by the computer system, efficacy of the treatmentby determining assessment of the state of cardiovascular disease of thesubject at a later point in time after treatment.

Embodiment 32: The computer-implemented method of Embodiment 24, whereinthe plaque volume comprises total plaque volume of the one or moreregions of plaque.

Embodiment 33: The computer-implemented method of Embodiment 24, whereinthe plaque volume comprises volume of non-calcified plaque of the one ormore regions of plaque.

Embodiment 34: The computer-implemented method of Embodiment 24, whereinthe plaque volume comprises volume of low density, non-calcified plaqueof the one or more regions of plaque.

Embodiment 35: The computer-implemented method of Embodiment 24, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), magnetic resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).

Embodiment 36: A computer-implemented method of assessing a state ofcardiovascular disease of a subject based on one or more normalizedplaque parameters derived from non-invasive medical image analysis, themethod comprising: accessing, by a computer system, a medical image of asubject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries; analyzing, by the computersystem, the medical image of the subject to identify one or more regionsof plaque within the one or more arteries; determining, by the computersystem, one or more plaque parameters associated with the identified oneor more regions of plaque, the one or more plaque parameters comprisingplaque volume of each of the one or more identified regions of plaqueand total plaque volume of the one or more identified regions of plaque;determining, by the computer system, a hypothetical vessel volume of oneof the one or more identified arteries without the one or more regionsof plaque; normalizing, by the computer system, percent atheroma volume(PAV) of one of the one or more identified arteries by generating ahypothetical PAV value based at least in part on the plaque volume ofone or more regions of plaque in the one of the one or more identifiedarteries and the hypothetical vessel volume of the one of the one ormore identified arteries without the one or more regions of plaque;determining, by the computer system, left ventricular mass of thesubject subtended by the one of the one or more arteries; normalizing,by the computer system, the plaque volume of the one or more regions ofplaque in the one of the one or more identified arteries by dividing theplaque volume of the one or more regions of plaque in the one of the oneor more identified arteries by the determined left ventricular mass ofthe subject subtended by the one of the one or more arteries;determining, by the computer system, total left ventricular mass of thesubject; normalizing, by the computer system, total plaque volume of theone or more regions of plaque by dividing the total plaque volume of theone or more regions of plaque by the determined total left ventricularmass of the subject; generating, by the computer system, a weightedmeasure of the normalized PAV of the one of the one or more identifiedarteries, the normalized plaque volume of the one or more regions ofplaque in the one of the one or more identified arteries, and thenormalized total plaque volume of the one or more regions of plaque;analyzing, by the computer system, the weighted measure by comparison toa dataset of reference weighted measure values of normalized PAV,normalized individual plaque volumes, and normalized total plaquevolumes derived from a plurality of medical images of a population withvarying states of cardiovascular disease; and determining, by thecomputer system, an assessment of a state of cardiovascular disease ofthe subject based at least in part on analysis of the weighted measure,wherein the computer system comprises a computer processor and anelectronic storage medium.

Embodiment 37: The computer-implemented method of Embodiment 36, furthercomprising generating, by the computer system, a treatment forcardiovascular disease for the subject based at least in part on thedetermined assessment of the state of cardiovascular disease.

Embodiment 38: The computer-implemented method of Embodiment 37, whereinthe treatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.

Embodiment 39: The computer-implemented method of Embodiment 38, furthercomprising tracking, by the computer system, efficacy of the treatmentby determining assessment of the state of cardiovascular disease of thesubject at a later point in time after treatment.

Embodiment 40: The computer-implemented method of Embodiment 37, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), magnetic resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).

Non-Invasive FFR

Disclosed herein are systems, devices, and methods for non-invasiveimage-based determination of fractional flow reserve (FFR) and/orischemia. In some embodiments, the systems, devices, and methods arerelated to FFR and/or ischemia analysis of arteries, such as coronary,aortic, and/or carotid arteries using one or more image analysistechniques. For example, in some embodiments, the systems, methods, anddevices can be configured to derive one or more stenosis and/or normalmeasurements from a medical image, which can be obtained non-invasively,and use the same to derive an assessment of FFR and/or ischemia. In someembodiments, the systems, methods, and devices can be configured toapply one or more allometric scaling laws to one or more stenosis and/ornormal measurements to derive and/or generate an assessment of FFRand/or ischemia.

Various systems, methods, and devices disclosed herein are directed toembodiments for addressing the foregoing issues. In particular, variousembodiments described herein relate to systems, devices, and methods fornon-invasive image-based determination of fractional flow reserve (FFR)and/or ischemia. In particular, in some embodiments, the systems,devices, and methods are related to FFR and/or ischemia analysis ofcoronary arteries using one or more image analysis techniques. Forexample, in some embodiments, the systems, methods, and devices can beconfigured to derive one or more stenosis and/or normal measurementsfrom a medical image, which can be obtained non-invasively, and use thesame to derive an assessment of FFR and/or ischemia. In someembodiments, the systems, methods, and devices can be configured toapply one or more allometric scaling laws to one or more stenosis and/ornormal measurements to derive and/or generate an assessment of FFRand/or ischemia.

In some embodiments, the systems, devices, and methods described hereinrelated to FFR and/or ischemia analysis are configured to utilizenon-invasive medical imaging technologies, such as a CT image or CCTAfor example, which can be inputted into a computer system configured toautomatically and/or dynamically analyze the medical image to identifyone or more coronary arteries and/or plaque within the same. Forexample, in some embodiments, the system can be configured to utilizeone or more machine learning and/or artificial intelligence algorithmsto automatically and/or dynamically analyze a medical image to identify,quantify, and/or classify one or more coronary arteries and/or plaque.In some embodiments, the system can be further configured to utilize theidentified, quantified, and/or classified one or more coronary arteriesand/or plaque to generate a treatment plan, track disease progression,and/or a patient-specific medical report, for example using one or moreartificial intelligence and/or machine learning algorithms In someembodiments, the system can be further configured to dynamically and/orautomatically generate a visualization of the identified, quantified,and/or classified one or more coronary arteries and/or plaque, forexample in the form of a graphical user interface. Further, in someembodiments, to calibrate medical images obtained from different medicalimaging scanners and/or different scan parameters or environments, thesystem can be configured to utilize a normalization device comprisingone or more compartments of one or more materials.

As will be discussed in further detail, the systems, devices, andmethods described herein allow for automatic and/or dynamic quantifiedanalysis of various parameters relating to plaque, cardiovasculararteries, and/or other structures. More specifically, in someembodiments described herein, a medical image of a patient, such as acoronary CT image or CCTA, can be taken at a medical facility. Ratherthan having a physician eyeball or make a general assessment of thepatient, the medical image is transmitted to a backend main server insome embodiments that is configured to conduct one or more analysesthereof in a reproducible manner. As such, in some embodiments, thesystems, methods, and devices described herein can provide a quantifiedmeasurement of one or more features of a coronary CT image usingautomated and/or dynamic processes. For example, in some embodiments,the main server system can be configured to identify one or morevessels, plaque, fat, and/or one or more measurements thereof from amedical image. Based on the identified features, in some embodiments,the system can be configured to generate one or more quantifiedmeasurements from a raw medical image, such as for example radiodensityof one or more regions of plaque, identification of stable plaque and/orunstable plaque, volumes thereof, surface areas thereof, geometricshapes, heterogeneity thereof, and/or the like. In some embodiments, thesystem can also generate one or more quantified measurements of vesselsfrom the raw medical image, such as for example diameter, volume,morphology, and/or the like. Based on the identified features and/orquantified measurements, in some embodiments, the system can beconfigured to generate a risk and/or disease state assessment and/ortrack the progression of a plaque-based disease or condition, such asfor example atherosclerosis, stenosis, and/or ischemia, using rawmedical images. Further, in some embodiments, the system can beconfigured to generate a visualization of a GUI of one or moreidentified features and/or quantified measurements, such as a quantizedcolor mapping of different features. In some embodiments, the systems,devices, and methods described herein are configured to utilize medicalimage-based processing to assess for a subject his or her risk of acardiovascular event, major adverse cardiovascular event (MACE), rapidplaque progression, and/or non-response to medication. In particular, insome embodiments, the system can be configured to automatically and/ordynamically assess such health risk of a subject by analyzing onlynon-invasively obtained medical images. In some embodiments, one or moreof the processes can be automated using an artificial intelligence (AI)and/or machine learning (ML) algorithm. In some embodiments, one or moreof the processes described herein can be performed within minutes in areproducible manner. This is stark contrast to existing measures todaywhich do not produce reproducible prognosis or assessment, takeextensive amounts of time, and/or require invasive procedures.

As discussed herein, disclosed herein are systems, devices, and methodsfor non-invasive image-based determination of fractional flow reserve(FFR) and/or ischemia. In particular, in some embodiments, the systems,devices, and methods are related to FFR and/or ischemia analysis ofarteries, such as coronary, aortic, and/or carotid arteries using one ormore image analysis techniques. For example, in some embodiments, thesystems, methods, and devices can be configured to derive one or morestenosis and/or normal measurements from a medical image, which can beobtained non-invasively, and use the same to derive an assessment of FFRand/or ischemia. In some embodiments, the systems, methods, and devicescan be configured to apply one or more allometric scaling laws to one ormore stenosis and/or normal measurements to derive and/or generate anassessment of FFR and/or ischemia.

In particular, in some embodiments, the systems, devices, and methodsdescribed herein can be configured to analyze one or more non-invasivelyobtained medical images of a subject, such as a CT image or CCTA, tonon-invasively determine PPR and/or ischemia. Generally speaking,invasive fractional flow reserve or invasive FFR is widely used toassess for hemodynamically significant coronary artery stenosis.However, such techniques require patients to undergo costly and invasivecoronary catheterization to measure invasive PPR and is therefore lessthan ideal. In some embodiments, computational fluid dynamics andcoronary CT angiography (CCTA) can be used as an alternative to invasivemeasures to determine the hemodynamic significance of stenoses, forexample via FFR_(CT). However, FFR_(CT) comes with many disadvantages.For example, FFR_(CT) can typically require large computational time,such as several hours, as well as large computational power andresources to complete. FFR_(CT) for physiologic assessment of CAD cantypically require off-site processing that can take several hours tocomplete. Further, FFR_(CT) can typically require high-quality images tobe able to generate a reliable outcome. As such, FFR_(CT) has manytechnical shortcomings that hinder its widespread use and adoption. Thesystems, methods, and devices described herein address such technicalshortcomings of existing technologies.

In particular, in some embodiments, the systems, methods, and devicesdescribed herein are configured to utilize one or more stenosis and/ornormal measurements derived from a medical image, such as a CT or CCTAimage, to non-invasively determine FFR and/or ischemia. In someembodiments, the systems, methods, and devices herein can analyze amedical image and generate FFR and/or ischemia prediction and/ormeasurements within a much shorter period of time, such as for examplein a matter of minutes, and/or require much less computational timeand/or resources compared to FFR_(CT). Further, in some embodiments, thesystems, methods, and devices described herein can be configured toutilize less than ideal quality images while still producing reliableprediction and/or assessment of PPR and/or ischemia in cases whereFFR_(CT) would not be a viable option due to the image quality.

More specifically, in some embodiments, the systems, methods, anddevices described herein can be configured to derive one or morestenosis and/or normal measurements from a medical image, such as forexample the mass and/or volume of a subtended myocardium at-risk distalto a stenosis. In some embodiments, subtended myocardium at-risk distalto a stenosis can provide reliable value in predicting hemodynamicallysignificant stenosis. In some embodiments, the mass and/or volume ofsubtended myocardium at-risk can be measured via CCTA. Further, in someembodiments, the mass and/or volume of subtended myocardium distal to astenosis can be related to coronary flow and/or FFR by utilizing one ormore allometric scaling principles.

As such, some of the systems, methods, and devices described hereinrelated to non-invasive determination of FFR and/or ischemia based onone or more measurements derived from a non-invasive medical image canprovide for drastically improved treatment of coronary artery disease(CAD) by stratifying patients based on the physiologic significance ofCAD, while avoiding the added cost and invasive nature of traditionalFFR as well as the large computational time and processing powerrequired of FFR_(CT).

More specifically, in some embodiments, the non-invasive FFR techniquesdescribed herein can allow for the physiologic assessment of a CADlesion non-invasively, for example via CCTA. In addition, in someembodiments, the non-invasive FFR techniques described herein canprovide a simplified and/or intuitive model for relating CCTA toinvasive FFR. Further, in some embodiments, the non-invasive FFRtechniques described herein can effectively calculate the equivalent ofinvasive FFR in seconds or minutes, as opposed to FFR_(CT) which canrequire hours of processing time. For example, in some embodiments, thesystems, methods, and devices described herein can generate theequivalent of invasive FFR within about 1 second, about 2 seconds, about3 seconds, about 4 seconds, about 5 second, about 10 seconds, about 15seconds, about 20 seconds, about 25 seconds, about 30 seconds, about 1minute, about 2 minutes, about 3 minutes, about 4 minutes, about 5minutes, about 10 minutes, about 20 minutes, about 30 minutes, and/orwithin a time period defined by two of the aforementioned values.Moreover, in some embodiments, the non-invasive FFR techniques describedherein can provide a more robust yet simplified solution and thus can beless prone to image artifacts and/or noise as compared to FFR_(CT).

As described herein, in some embodiments, the systems, methods, anddevices can be configured to use allometric scaling laws to relatemorphological measurements of coronary arteries to physiologicalmeasurements, such as coronary flow and/or the equivalent to invasiveFFR, for example via a power scaling relationship. In some embodiments,morphological measurements can include, for example, coronary lumenarea, mass or volume of myocardium subtended by a coronary stenosis,coronary lumen volume subtended by a coronary stenosis, coronary vessellength, and/or the like. In some embodiments, one or more or allmorphological measurements can be obtained from analyzing a CT imageand/or CCTA. In some embodiments, the systems, methods, and devicesdescribed herein can be configured to utilize a mathematicalrelationship between coronary lumen area, subtended myocardial mass,and/or invasive FFR, non-invasive, FFR, or equivalent thereof. In someembodiments, the systems, methods, and devices described herein can beconfigured to utilize one or more other morphological measurements, suchas for example coronary lumen volume, coronary vessel length, and/or thelike, as well as a mathematical relationship between the same and/orinvasive FFR, non-invasive FFR, or equivalent thereof.

Relating Lumen Area and Myocardial Mass to FFR

For illustrative purposes, FIG. 15A provides a schematic illustrating anexample embodiment(s) of systems, devices, and methods for non-invasiveimage-based determination of fractional flow reserve (FFR) and/orischemia. As illustrated in FIG. 15A, in some embodiments, the systems,methods, and devices described herein can be configured to relate lumenarea and myocardial mass to FFR and utilize the same for determining FFRand/or ischemia from analyzing a non-invasively obtained medical image.FIG. 15A depicts a left ventricle 1500 including a coronary arterycenter line 1502 and a left ventricle myocardium 1504. The leftventricle 1500 may, in some instances, include a lesion 1506 on thecoronary artery centerline 1502. The lesion 1506 may affect a lumen witha lumen area 1508 that is useful in determining FFR. The left ventriclemyocardium 1504 can, in some instances, become myocardial mass 1510 thatis subtended by the lumen area 1508 at the lesion 1506. In someembodiments, measurements of the left ventricle 1500 can be used topredict PPR.

In particular, as illustrated in FIG. 15A, in some embodiments,allometric scaling laws can be used to relate coronary lumen area (A)and the myocardial mass 1510 subtended by a coronary lesion 1506 (M) topredict FFR or invasive FFR by generating a non-invasive FFR, allometricFFR, or myocardium FPR. In some embodiments, based on allometric scalinglaws, FPR or invasive FFR can be approximated by generating anon-invasive FPR, allometric FFR, or myocardium FFR via the Equation 1below:

$\begin{matrix}{{FFR}_{{non} - {invasive}} = {Y_{0}*\frac{A^{b}A}{M^{b}M}}} & {{Equation}1}\end{matrix}$

In some embodiments, the exponential coefficients (b_(A) and b_(M))and/or the constant (Y₀) can be derived from the power relationshipbetween lumen area, myocardial mass, and coronary flow. For example, insome embodiments, the system can be configured to derive the exponentialcoefficients (b_(A) and b_(M)) and/or the constant (Y₀) from existingdata comprising coronary lumen area (A), myocardial mass subtended by acoronary lesion (M), and invasive PPR. In some embodiments, using thedetermined exponential coefficients (b_(A) and b_(M)) and/or theconstant (Y₀), the system can be configured to apply the equation aboveto coronary lumen area (A) and myocardial mass subtended by a coronarylesion (M) derived from a prospective image to determine or estimate FFRnon-invasively without using FFR_(CT). Further, in some embodiments, Y₀,b_(A), and b_(M) are derived from analysis of retrospective medicalimages and invasive FFR measurements (FFR_(invasive)).

Determining Coefficients to Relate Coronary Lumen Area and MyocardialMass to PPR

More specifically, in some embodiments, the system can be configured todetermine exponential coefficients (b_(A) and b_(M)) and/or the constant(Y₀) by utilizing the relationship between invasive FFR, lumen area, andmyocardial mass. In particular, in some embodiments, the exponentialcoefficients (b_(A) and b_(M)) and/or the constant (Y₀) can becalculated via multiple linear regression, following application of alogarithmic transformation. The derivation of a linear equation relatinginvasive FFR with lumen area and myocardial mass in some embodiments isprovided below.

In some embodiments, in terms of coronary blood flow, invasive FFR(FFR_(invasive)) can be defined as the ratio of blood flow across astenosis (Q_(Stenosis)) to the blood flow across the same coronaryregion in the absence of stenosis (Q_(Normal)) as set forth below inEquation 2.

$\begin{matrix}{{FFR}_{invasive} = \frac{Q_{Stenosis}}{Q_{Normal}}} & {{Equation}2}\end{matrix}$

In some embodiments, blood across a stenosis (Q_(Stenosis)) can berelated to the lumen area (A) at the point of the stenosis viaallometric scaling using a power law as set forth below in Equation 3.

Q _(Stenosis) =Y _(A) *A ^(b) ^(A)   Equation 3

In some embodiments, blood flow in the absence of a stenosis(Q_(Normal)) can be related to the myocardial mass subtended by thestenosis (M) via allometric scaling using a power law as set forth belowin Equation 4.

Q _(Normal) =Y _(M) *M ^(b) ^(M)   Equation 4

In some embodiments, by substituting Equation 3 and 4 into Equation 1,lumen area (A) and subtended myocardial mass (M) can be related toinvasive FFR as set forth below in Equation 5.

$\begin{matrix}{{FFR}_{invasive} = \frac{Y_{A}*A^{b}A}{Y_{M}*M^{b}M}} & {{Equation}5}\end{matrix}$

In some embodiments, the power law constant for lumen area (YA) andsubtended myocardial mass (YM) can be combined into a single constant(Y0) as set forth below in Equation 6.

$\begin{matrix}{{FFR}_{invasive} = {Y_{0}\frac{A^{b}A}{M^{b}M}}} & {{Equation}6}\end{matrix}$

In some embodiments, a log transform can be applied to Equation 6 as setforth below in Equation 7.

$\begin{matrix}{{\log({FFR})} = {\log( {Y_{0}\frac{A^{b}A}{M^{b}M}} )}} & {{Equation}7}\end{matrix}$

In some embodiments, following the log transform, Equation 7 can beexpanded, providing a linear equation relating lumen area (A) andsubtended myocardial mass (M) to invasive FFR as set forth below inEquation 8.

log(FFR)=b _(A)*log(A)−b _(M)*log(M)+log(Y ₀)  Equation 8

In some embodiments, utilizing one or more of Equations 2-8, the systemcan be configured to determine exponential coefficients (b_(A) andb_(M)) and/or the constant (Y₀), for example by applying data sets withknown invasive FFR, lumen area, and/or myocardial mass.

Validation

A non-limiting, illustrative study was performed to validatenon-invasive PPR, allometric FFR, or myocardium FFR as described herein.In particular, as described herein, some embodiments of the systems,methods, and devices are configured to determine the equivalent to an“invasive” FFR using lumen area and subtended myocardial mass from CCTA.More specifically, in some embodiments, the systems, methods, anddevices are configured to calculate an “invasive” FFR equivalent fromCCTA using only subtended myocardial mass and minimal lumen area. FIGS.15B-15C are schematics illustrating results of the study utilizing anexample embodiment(s) of systems, devices, and methods for non-invasiveimage-based determination of fractional flow reserve (FFR) and/orischemia.

In the study, a subset of 169 patients from a multicenter internationalstudy were selected for post-hoc analysis. In the study, all patientsunderwent CCTA and invasive coronary angiography with invasive FFR. Inthe study, 234 coronary territories were assessed with invasive FFR.Quantitative CT was performed by a validated software as a service.

In the study, territories were split into a training and testing cohortof 163 territories and 71 territories respectively. Invasive FFR wasempirically related to the Lumen Area (b_Lumen) divided by SubtendedMyocardial Mass (b_Myo). The training cohort was used to deriveexponential coefficients b_Lumen and b_Myo using multiple linearregression, for example utilizing one or more of Equations 2-8 herein.The performance of the resulting non-invasive FFR, allometric FFR, ormyocardial FPR based on the determined exponential coefficients and/orconstant was assessed by prospectively applying b_Lumen and b_Myo to thetesting cohort. The performance of the resulting non-invasive FFR,allometric FFR, or myocardial FFR to predict ischemia was directlycompared with FFR_(CT) and an AI-enabled CCTA diameter stenosis(DSAt_ocT) measurement.

In the study, for all patients, the mean subtended myocardial massdistal from the minimal lumen area lesion for ischemic and non-ischemicterritories was 35.85±20.85 g and 27.51±18.96 g (p-value<0.05). Theminimal lumen area for ischemic and non-ischemic territories was1.15±0.88 g and 2.52±1.51 g (p-value<0.05).

In the study, FFR_(CT) failed in 17 test cohort cases due to inadequateimage quality. The 17 territories wherein FFR_(CT) failed were stillincluded for evaluation of non-invasive PPR, allometric FFR, ormyocardial FFR and DS_(AI-QCT). b_Lumen and b_Myo were (p-value<0.05)and 0.08 (p-value<0.05) respectively, with a constant Y of 0.96. In thestudy, using only the testing cohort non-invasive FFR, allometric FFR,or myocardial FFR (FFR_(non-invasive)) was related to invasive FFR byFFR_(non-invasive)=0.41*FFR_(invasive)+0.47(R²=0.37, RMSE=0.12). In thestudy, FFR_(CT) was related to invasive FFR byFFR_(CT)=0.53*FFR+0.30(R²=0.23, RMSE=0.18). Further, in the study,DS_(SI-QCT) was related to invasive FFR by 1−DS_(AI-QCT)=0.82*FFR−0.09(R²=0.32, RMSE=0.29). In the study, the area under the receiver operatorcharacteristic curve to predict invasive FFR≤0.80 forFFR_(non-invasive), FFR_(CT), and DS_(AI-QCT) was 0.82, 0.84, and 0.81respectively.

As such, subtended myocardial mass may improve the non-invasiveassessment of ischemia, as used in some embodiments of the systems,devices, and methods described herein. As shown in the study, in someembodiments, non-invasive FFR, allometric FFR, or myocardial FFR canprovide a similar measurement of ischemia as PPRc T that is lesssusceptible to image quality.

Alternative Variables to Derive Non-Invasive FFR

In some embodiments, the systems, methods, and devices described hereincan be configured to utilize one or more alternative measurements toderive non-invasive FFR using allometric laws. For example, in someembodiments, the system can be configured to utilize one or more othervariables from CCTA imaging to determine non-invasive FPR. Figure is aschematic illustrating an example embodiment(s) of systems, devices, andmethods for non-invasive image-based determination of fractional flowreserve (FFR) and/or ischemia.

As illustrated in FIG. 15D, in some embodiments, the systems, methods,and devices can be configured to utilize one or more variables from CCTAimaging to determine non-invasive FFR and/or allometric FFR, for examplebased on equations in column (a). In some embodiments, the systems,methods, and devices described herein can be configured to utilize oneor more stenosis measurements, as depicted in column (b), and/or one ormore normal measurements, as depicted in column (c).

In particular, in some embodiments, stenosis measurements can includeone or more of subtended coronary lumen volume, minimal lumen area,minimal lumen diameter, distal lumen diameter, and/or the like. In someembodiments, distal lumen diameter can refer to the diameter beyond thecoronary stenosis of interest. In some embodiments, one or more or allmeasurements noted in column (b) can be considered estimates of theextent of coronary obstruction. As such, in some embodiments, one ormore or all measurements noted in column (b) may change in the presenceof obstruction.

In some embodiments, normal measurements can include subtended coronarylength, subtended myocardium, proximal lumen diameter, proximal lumenarea, and/or the like. In some embodiments, subtended coronary lengthcan refer to the length of the coronary tree distal to the coronarystenosis of interest. In some embodiments, proximal lumen diameterand/or area can be measured at any point along the coronary treeproximal to the coronary lesion of interest and/or within a portion ofthe coronary tree unaffected by the coronary stenosis of interest.

Non-Invasive FFR Example Systems, Methods, and Devices

As discussed herein, in some embodiments, the systems, methods, anddevices can be configured to determine non-invasive FFR or allometricFFR based on one or more stenosis and/or normal measurements derivedfrom one or more medical images which can be obtained non-invasively.FIG. 15E is a flowchart illustrating an example embodiment(s) ofsystems, devices, and methods for non-invasive image-based determinationof fractional flow reserve (FFR) and/or ischemia.

In particular, as illustrated in FIG. 15E, in some embodiments, thesystem can be configured to access one or more training medical imagesat block 1512. In some embodiments, the medical image can include one ormore arteries, such as coronary, carotid, and/or other arteries of asubject. In some embodiments, the medical image can be stored in amedical image database 1514A. In some embodiments, the medical imagedatabase 1514A can be locally accessible by the system and/or can belocated remotely and accessible through a network connection. Themedical image can comprise an image obtain using one or more modalitiessuch as for example, CT, Dual-Energy Computed Tomography (DECT),Spectral CT, photon-counting CT, x-ray, ultrasound, echocardiography,intravascular ultrasound (IVUS), Magnetic Resonance (MR) imaging,optical coherence tomography (OCT), nuclear medicine imaging,positron-emission tomography (PET), single photon emission computedtomography (SPECT), or near-field infrared spectroscopy (NIRS). In someembodiments, the medical image comprises one or more of acontrast-enhanced CT image, non-contrast CT image, MR image, and/or animage obtained using any of the modalities described above.

In some embodiments, the system can be configured to automaticallyand/or dynamically perform one or more analyses of the medical image asdiscussed herein. For example, in some embodiments, at block 1516, thesystem can be configured to identify one or more vessels, such as of oneor more arteries. The one or more arteries can include coronaryarteries, carotid arteries, aorta, renal artery, lower extremity artery,upper extremity artery, and/or cerebral artery, amongst others. In someembodiments, the system can be configured to utilize one or more AIand/or ML algorithms to automatically and/or dynamically identify one ormore arteries or coronary arteries using image processing. For example,in some embodiments, the one or more AI and/or ML algorithms can betrained using a Convolutional Neural Network (CNN) on a set of medicalimages on which arteries or coronary arteries have been identified,thereby allowing the AI and/or ML algorithm automatically identifyarteries or coronary arteries directly from a medical image. In someembodiments, the arteries or coronary arteries are identified by sizeand/or location.

In some embodiments, at block 1518, the system can be configured toderive one or more stenosis and/or normal measurements from theidentified vessels. For example, in some embodiments, the one or morestenosis measurements can include lumen area, subtended coronary lumenvolume, minimal lumen area, minimal lumen diameter, distal lumendiameter, and/or the like. In some embodiments, the one or more normalmeasurements can include subtended coronary length, subtended myocardiummass and/or volume, proximal lumen diameter, proximal lumen area, and/orthe like. In some embodiments, the system can be configured to utilizeone or more AI and/or ML algorithms to automatically and/or dynamicallyderive one or more stenosis and/or normal measurements using imageprocessing. For example, in some embodiments, the one or more AI and/orML algorithms can be trained using a Convolutional Neural Network (CNN)on a set of medical images on which one or more stenosis and/or normalmeasurements have been derived, thereby allowing the AI and/or MLalgorithm to automatically derive one or more stenosis and/or normalmeasurements directly from a medical image.

In some embodiments, at block 1520, the system can be configured toaccess invasive PPR data or measurements or otherwise obtained FFRmeasurements for the one or more training medical images. In someembodiments, based in part on the accessed FFR measurements, the systemat block 1522 can be configured to derive and/or determine one or moreconstants and/or coefficients, for example of one or more mathematicalequations describing the relationship between FPR and the one or morestenosis and/or normal measurements, thereby establishing a usable modelto predict and/or determine FFR non-invasively based on the one or morestenosis and/or normal measurements of new cases. As an example, in someembodiments, the system can be configured to utilize a linear regressionmodel to determine the one or more constants and/or coefficients.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1512-1522 to improve accuracyof the model and/or determined constants and/or coefficients. Forexample, in some embodiments, the system can be configured to repeat oneor more processes described in relation to blocks 1512-1522 for one ormore additional images, vessels, segments, and/or at different times. Assuch, in some embodiments, the system can be configured to continuouslyimprove accuracy of the model for determining FFR non-invasively usingonly one or more stenosis and/or normal measurements derived from amedical image.

In some embodiments, at block 5124, the system can be configured toaccess one or more prospective medical images. In some embodiments, theprospective medical image can include one or more arteries, such ascoronary, carotid, and/or other arteries of a subject. In someembodiments, the prospective medical image can be stored in a medicalimage database 1514B, which can be the same and/or different databasefrom the medical image database 1514A. In some embodiments, the medicalimage database 1514B can be locally accessible by the system and/or canbe located remotely and accessible through a network connection. Theprospective medical image can comprise an image obtain using one or moremodalities such as for example, CT, Dual-Energy Computed Tomography(DECT), Spectral CT, photon-counting CT, x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), Magnetic Resonance(MR) imaging, optical coherence tomography (OCT), nuclear medicineimaging, positron-emission tomography (PET), single photon emissioncomputed tomography (SPECT), or near-field infrared spectroscopy (NIRS).In some embodiments, the prospective medical image comprises one or moreof a contrast-enhanced CT image, non-contrast CT image, MR image, and/oran image obtained using any of the modalities described above.

In some embodiments, at block 1526, the system can be configured toderive one or more stenosis and/or normal measurements from one or morevessels identified on the prospective image. In some embodiments, one ormore vessels can be identified on the prospective image using one ormore techniques and/or features described above in relation to block1516. In some embodiments, the one or more stenosis and/or normalmeasurements can be derived by the system using one or more techniquesand/or features described above in relation to block 1518. For example,similar to the features described above in relation to block 1518, insome embodiments, the one or more stenosis measurements can includelumen area, subtended coronary lumen volume, minimal lumen area, minimallumen diameter, distal lumen diameter, and/or the like. Similarly, insome embodiments, the one or more normal measurements can includesubtended coronary length, subtended myocardium mass and/or volume,proximal lumen diameter, proximal lumen area, and/or the like.

In some embodiments, at block 1528, the system can be configured todetermine non-invasive FFR based on the derived one or more stenosisand/or normal measurements. For example, in some embodiments, the systemcan be configured to apply the derived one or more stenosis and/ornormal measurements to the mathematical model generated based on the oneor more constants and/or coefficients determined in block 1522. Asdescribed herein, in some embodiments, the non-invasive FFR can bedetermined based on application of allometric scaling laws.

In some embodiments, at block 1530, the system can be configured togenerate non-invasive assessment of ischemia based on the determinednon-invasive FFR. For example, in some embodiments, the system can beconfigured to compare the determined non-invasive FFR to one or morereference values to generate assessment of ischemia based on benchmarkvalues. In some embodiments, the reference values can be stored in areference values database 1532, which can be locally accessible by thesystem and/or can be located remotely and accessible through a networkconnection. In some embodiments, the system can be configured to utilizeone or more AI and/or ML algorithms to automatically and/or dynamicallygenerate an assessment of ischemia based on benchmark reference values.In some embodiments, the one or more reference values can be derivedfrom other subjects with varying states or risks of cardiovasculardisease, including for example normal values. In some embodiments, theone or more reference values can be obtained from one or more medicalimages using the same or similar imaging modalities as the medical imageaccessed at block 1512. In some embodiments, the one or more referencevalues can be obtained from analyzing one or more medical images togenerate one or more non-invasive FFR values, for example using one ormore processes described in relation to blocks 1512-1528.

In some embodiments, based on such assessment of ischemia and/ornon-invasive FFR, the system, at block 1534, can be configured togenerate a graphical representation of the analysis results. In someembodiments, the system can be configured to generate a graphicalgeneration reporting the analysis results on a vessel-by-vessel basisand/or subject basis. For example, in some embodiments, the system canbe configured to generate a graphical representation of one or morearteries or vessels, in which specific arteries and/or vessels can becolor-coded or assigned some value or other indicator depending on theanalysis results. As an illustrative example, in some embodiments, ifthe system determines that ischemia or non-invasive FFR of a particularvessel is high, the system can be configured to color code that vesselred in the graphical representation. In some embodiments, the assessmentof ischemia is generated based at least in part on comparison to one ormore reference values of non-invasive FFR.

In some embodiments, based on such assessment of ischemia and/ornon-invasive FFR, the system, at block 1536, can be configured todetermine a risk or state of cardiovascular disease or health of thesubject. Further, in some embodiments, based on such analysis, thesystem, at block 1536, can be configured to determine a proposedtreatment for the subject. The treatment can include, for example,medical treatment such as statins, interventional treatment such asstent implantation, and/or lifestyle treatment such as exercise or diet.In some embodiments, in determining the risk or state of cardiovasculardisease or health and/or treatment, the system can access arisk/treatment database 1538, which can be locally accessible by thesystem and/or can be located remotely and accessible through a networkconnection. In some embodiments, the risk/treatment database 1538 caninclude reference points or data that relate one or more treatment tocardiovascular disease risk or state determined based on one or morereference non-invasive FFR and/or ischemia values.

In some embodiments, the system can be configured to repeat one or moreprocesses described in relation to blocks 1512-1536, for example for oneor more other vessels, segment, regions of plaque, different subjects,and/or for the same subject at a different time. As such, in someembodiments, the system can provide for longitudinal disease trackingand/or personalized treatment for a subject.

Computer System

In some embodiments, the systems, processes, and methods describedherein are implemented using a computing system, such as the oneillustrated in FIG. 15F. The example computer system 1540 is incommunication with one or more computing systems 1558 and/or one or moredata sources 1560 via one or more networks 1556. While FIG. 15Fillustrates an embodiment of a computing system 1540, it is recognizedthat the functionality provided for in the components and modules ofcomputer system 1540 can be combined into fewer components and modules,or further separated into additional components and modules.

The computer system 1540 can comprise a Non-Invasive FFR/IschemiaAssessment Module 1552 that carries out the functions, methods, acts,and/or processes described herein. The Non-Invasive FFR/IschemiaAssessment Module 1552 executed on the computer system 1540 by a centralprocessing unit 1544 discussed further below. Other features of thecomputer system 1540 can be similar to corresponding features of thecomputer system of FIG. 9G, described above.

Certain Examples of Embodiments of Non-Invasive FFR

The following are non-limiting examples of certain embodiments ofsystems and methods of non-invasive FFR introduction. Other embodimentsmay include one or more other features, or different features, that arediscussed herein.

Embodiment 1: A computer-implemented method of determination offractional flow reserve and assessment of ischemia based at least inpart on one or more measurements derived from non-invasive medical imageanalysis, the computer-implemented method comprising: accessing, by acomputer system, a medical image of a subject, wherein the medical imageof the subject is obtained non-invasively; analyzing, by the computersystem, the medical image of the subject to identify one or morecoronary arteries; determining, by the computer system, one or morestenosis measurements based on analysis of the identified one or morecoronary arteries in the medical image of the subject, the one or morestenosis measurements comprising coronary lumen area (A) and subtendedmyocardial mass (M); determining, by the computer system, non-invasivefractional flow reserve (FFR_(non-invasive)) for the identified one ormore coronary arteries based at least in part by applying the one ormore stenosis measurements to

${{FFR}_{{non} - {invasive}} = {Y_{0}*\frac{A^{b}A}{M^{b}M}}},$

wherein Y₀ comprises a constant, and wherein b_(A) and b_(M) compriseexponential coefficients; and generating, by the computer system, anassessment of ischemia based at least in part on the determinednon-invasive fractional flow reserve for the identified one or morecoronary arteries, wherein the computer system comprises a computerprocessor and an electronic storage medium.

Embodiment 2: The computer-implemented method of Embodiment 1, whereinthe determined non-invasive fractional flow reserve is determined usingallometric scaling.

Embodiment 3: The computer-implemented method of Embodiment 1, whereinY₀, b_(A), and b_(M) are derived from analysis of retrospective medicalimages and invasive FFR measurements (FFR_(invasive)).

Embodiment 4: The computer-implemented method of Embodiment 1, whereinY₀, b_(A), and b_(M) are derived using an equation relating coronarylumen area (A) and subtended myocardial mass (M) to FFR_(invasive).

Embodiment 5: The computer-implemented method of Embodiment 4, whereinthe equation comprises

${FFR}_{invasive} = {Y_{0}*{\frac{A^{b}A}{M^{b}M}.}}$

Embodiment 6: The computer-implemented method of Embodiment 4, whereinthe equation comprises log(FFR_(invasive))=b_(A)*log(A)−b_(M)*log(M)+log (Y₀).

Embodiment 7: The computer-implemented method of Embodiment 1, whereinthe coronary lumen area comprises minimal lumen area.

Embodiment 8: The computer-implemented method of Embodiment 1, whereinthe generated assessment of ischemia is generated based at least in parton comparison to one or more reference values of the determinednon-invasive fractional flow reserve.

Embodiment 9: The computer-implemented method of Embodiment 1, furthercomprising generating, by the computer system, a graphicalrepresentation of the generated assessment of ischemia for theidentified one or more coronary arteries.

Embodiment 10: The computer-implemented method of Embodiment 1, furthercomprising generating, by the computer system, a recommended treatmentfor the subject based at least in part on the generated assessment ofischemia for the one or more coronary arteries.

Embodiment 11: A computer-implemented method of determination offractional flow reserve and assessment of ischemia based at least inpart on one or more measurements derived from non-invasive medical imageanalysis, the computer-implemented method comprising: accessing, by acomputer system, a medical image of a subject, wherein the medical imageof the subject is obtained non-invasively; analyzing, by the computersystem, the medical image of the subject to identify one or morecoronary arteries; determining, by the computer system, one or morestenosis measurements (S) based on analysis of the identified one ormore coronary arteries in the medical image of the subject, the one ormore stenosis measurements (S) comprising one or more of subtendedcoronary lumen volume, minimal lumen area, minimal lumen diameter, ordistal lumen diameter; determining, by the computer system, one or morenormal measurements (N) based on analysis of the identified one or morecoronary arteries in the medical image of the subject, the one or morenormal measurements (N) comprising one or more of subtended coronarylength, subtended myocardium, proximal lumen diameter, or proximal lumenarea; determining, by the computer system, non-invasive fractional flowreserve (FFR_(non-invasive)) for the identified one or more coronaryarteries based at least in part by applying the one or more stenosismeasurements (S) and the one or more normal measurements (N) to

${{FFR}_{{non} - {invasive}} = \frac{Y_{S}*S^{b\_ S}}{Y_{N}*N^{b\_ N}}},$

wherein Y_(S) comprises a constant, and wherein b_S and b_N compriseexponential coefficients; and generating, by the computer system, anassessment of ischemia based at least in part on the determinednon-invasive fractional flow reserve for the identified one or morecoronary arteries, wherein the computer system comprises a computerprocessor and an electronic storage medium.

Embodiment 12: The computer-implemented method of Embodiment 11, whereinthe determined non-invasive fractional flow reserve is determined usingallometric scaling.

Embodiment 13: The computer-implemented method of Embodiment 11, whereinY_(S), b_s, and b_N are derived from analysis of retrospective medicalimages and invasive FFR measurements (FFR_(invasive)).

Embodiment 14: The computer-implemented method of Embodiment 13, whereinY_(S), b_s, and b_N are derived from using

${FFR}_{invasive} = {\frac{Y_{S}*S^{b\_ S}}{Y_{N}*N^{b\_ N}}.}$

Embodiment 15: The computer-implemented method of Embodiment 11, whereinthe distal lumen diameter comprises a diameter beyond an area ofcoronary stenosis of interest in the identified one or more coronaryarteries.

Embodiment 16: The computer-implemented method of Embodiment 11, whereinthe proximal lumen diameter and proximal lumen area are measured at apoint along a coronary tree proximal to a coronary lesion of interest,wherein the point along the coronary tree is unaffected by coronarystenosis of interest.

Embodiment 17: The computer-implemented method of Embodiment 11, whereinthe generated assessment of ischemia is generated based at least in parton comparison to one or more reference values of the determinednon-invasive fractional flow reserve.

Embodiment 18: The computer-implemented method of Embodiment 11, furthercomprising generating, by the computer system, a graphicalrepresentation of the generated assessment of ischemia for theidentified one or more coronary arteries.

Embodiment 19: The computer-implemented method of Embodiment 11, furthercomprising generating, by the computer system, a recommended treatmentfor the subject based at least in part on the generated assessment ofischemia for the identified one or more coronary arteries.

Embodiment 20: The computer-implemented method of Embodiment 11, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, magnetic resonance (MR) imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Embodiment 21: A system for determining fractional flow reserve andassessment of ischemia based at least in part on one or moremeasurements derived from non-invasive medical image analysis, thesystem comprising: one or more computer readable storage devicesconfigured to store a plurality of computer executable instructions; andone or more hardware computer processors in communication with the oneor more computer readable storage devices and configured to execute theplurality of computer executable instructions in order to cause thesystem to: access a medical image of a subject, wherein the medicalimage of the subject is obtained non-invasively; analyze the medicalimage of the subject to identify one or more coronary arteries;determine one or more stenosis measurements based on analysis of theidentified one or more coronary arteries in the medical image of thesubject, the one or more stenosis measurements comprising coronary lumenarea (A) and subtended myocardial mass (M); determine non-invasivefractional flow reserve (FFR_(non-invasive)) for the identified one ormore coronary arteries based at least in part by applying the one ormore stenosis measurements to

${{FFR}_{{non} - {invasive}} = {Y_{0}*\frac{A^{b}A}{M^{b}M}}},$

wherein Y₀ comprises a constant, and wherein b_(A) and b_(M) compriseexponential coefficients; and generate an assessment of ischemia basedat least in part on the determined non-invasive fractional flow reservefor the identified one or more coronary arteries.

Embodiment 22: The system of Embodiment 21, wherein the determinednon-invasive fractional flow reserve is determined using allometricscaling.

Embodiment 23: The system of Embodiment 21, wherein Y₀, b_(A), and b_(M)are derived from analysis of retrospective medical images and invasiveFFR measurements (FFR_(invasive)).

Embodiment 24: The system of Embodiment 21, wherein Y₀, b_(A), and b_(M)are derived using an equation relating coronary lumen area (A) andsubtended myocardial mass (M) to FFR_(invasive).

Embodiment 25: The system of Embodiment 24, wherein the equationcomprises

${FFR}_{invasive} = {Y_{0}*{\frac{A^{b}A}{M^{b}M}.}}$

Embodiment 26: The system of Embodiment 24, wherein the equationcomprises log(FFR_(invasive))=b_(A)*log(A)−b_(M)*log(M)+log(Y₀).

Embodiment 27: The system of Embodiment 21, wherein the coronary lumenarea comprises minimal lumen area.

Embodiment 28: The system of Embodiment 21, wherein the generatedassessment of ischemia is generated based at least in part on comparisonto one or more reference values of the determined non-invasivefractional flow reserve.

Embodiment 29: The system of Embodiment 21, wherein the system isfurther caused to generate a graphical representation of the generatedassessment of ischemia for the identified one or more coronary arteries.

Embodiment 30: The system of Embodiment 21, wherein the system isfurther caused to generate a recommended treatment for the subject basedat least in part on the generated assessment of ischemia for theidentified one or more coronary arteries.

Embodiment 31: A computer-implemented method of determination offractional flow reserve and assessment of ischemia based at least inpart on one or more measurements derived from non-invasive medical imageanalysis, the computer-implemented method comprising: accessing, by acomputer system, a medical image of a subject, wherein the medical imageof the subject is obtained non-invasively; analyzing, by the computersystem, the medical image of the subject to identify one or morecoronary arteries; determining, by the computer system, one or moremeasurements (n) based on analysis of the identified one or morecoronary arteries in the medical image of the subject, the one or moremeasurements (n) comprising one or more of coronary artery disease (CAD)variables, lumen area, subtended lumen volume, subtended myocardium,minimal lumen area, minimal lumen diameter, distal lumen diameter,subtended coronary length, proximal lumen diameter, or proximal lumenarea; determining, by the computer system, non-invasive fractional flowreserve (FFR_(non-invasive)) for the identified one or more coronaryarteries based at least in part by applying the one or more measurements(n) to FFR_(non-invasive)=Π_(n) ^(CAD vars.) Y_(n)*n^(b) ^(n) , whereinY_(n) comprises a constant, and wherein b_(n) comprises an exponentialcoefficient; and generating, by the computer system, an assessment ofischemia based at least in part on the determined non-invasivefractional flow reserve for the identified one or more coronaryarteries, wherein the computer system comprises a computer processor andan electronic storage medium.

Embodiment 32: The computer-implemented method of Embodiment 31, whereinthe determined non-invasive fractional flow reserve is determined usingallometric scaling.

Embodiment 33: The computer-implemented method of Embodiment 31, whereinY_(n), and b_(n) are derived from analysis of retrospective medicalimages and invasive FFR measurements (FFR_(invasive)).

Embodiment 34: The computer-implemented method of Embodiment 31, whereinthe distal lumen diameter comprises a diameter beyond an area ofcoronary stenosis of interest in the identified one or more coronaryarteries.

Embodiment 35: The computer-implemented method of Embodiment 31, whereinthe proximal lumen diameter and proximal lumen area are measured at apoint along a coronary tree proximal to a coronary lesion of interest,wherein the point along the coronary tree is unaffected by coronarystenosis of interest.

Embodiment 36: The computer-implemented method of Embodiment 31, whereinthe generated assessment of ischemia is generated based at least in parton comparison to one or more reference values of the determinednon-invasive fractional flow reserve.

Embodiment 37: The computer-implemented method of Embodiment 31, furthercomprising generating, by the computer system, a graphicalrepresentation of the generated assessment of ischemia for the one ormore coronary arteries.

Embodiment 38: The computer-implemented method of Embodiment 31, furthercomprising generating, by the computer system, a recommended treatmentfor the subject based at least in part on the generated assessment ofischemia for the identified one or more coronary arteries.

Embodiment 39: The computer-implemented method of Embodiment 31, whereinthe medical image is obtained using an imaging technique comprising oneor more of computed tomography (CT), x-ray, ultrasound,echocardiography, magnetic resonance (MR) imaging, optical coherencetomography (OCT), nuclear medicine imaging, positron-emission tomography(PET), single photon emission computed tomography (SPECT), or near-fieldinfrared spectroscopy (NIRS).

Other Embodiment(s)

Although this invention has been disclosed in the context of certainembodiments and examples, it will be understood by those skilled in theart that the invention extends beyond the specifically disclosedembodiments to other alternative embodiments and/or uses of theinvention and obvious modifications and equivalents thereof. Inaddition, while several variations of the embodiments of the inventionhave been shown and described in detail, other modifications, which arewithin the scope of this invention, will be readily apparent to those ofskill in the art based upon this disclosure. It is also contemplatedthat various combinations or sub-combinations of the specific featuresand aspects of the embodiments may be made and still fall within thescope of the invention. It should be understood that various featuresand aspects of the disclosed embodiments can be combined with, orsubstituted for, one another in order to form varying modes of theembodiments of the disclosed invention. Any methods disclosed hereinneed not be performed in the order recited. Thus, it is intended thatthe scope of the invention herein disclosed should not be limited by theparticular embodiments described above.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. Theheadings used herein are for the convenience of the reader only and arenot meant to limit the scope of the inventions or claims.

Further, while the methods and devices described herein may besusceptible to various modifications and alternative forms, specificexamples thereof have been shown in the drawings and are hereindescribed in detail. It should be understood, however, that theinvention is not to be limited to the particular forms or methodsdisclosed, but, to the contrary, the invention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the various implementations described and the appendedclaims. Further, the disclosure herein of any particular feature,aspect, method, property, characteristic, quality, attribute, element,or the like in connection with an implementation or embodiment can beused in all other implementations or embodiments set forth herein. Anymethods disclosed herein need not be performed in the order recited. Themethods disclosed herein may include certain actions taken by apractitioner; however, the methods can also include any third-partyinstruction of those actions, either expressly or by implication. Theranges disclosed herein also encompass any and all overlap, sub-ranges,and combinations thereof. Language such as “up to,” “at least,” “greaterthan,” “less than,” “between,” and the like includes the number recited.Numbers preceded by a term such as “about” or “approximately” includethe recited numbers and should be interpreted based on the circumstances(e.g., as accurate as reasonably possible under the circumstances, forexample ±5%, ±10%, ±15%, etc.). For example, “about 3.5 mm” includes“3.5 mm” Phrases preceded by a term such as “substantially” include therecited phrase and should be interpreted based on the circumstances(e.g., as much as reasonably possible under the circumstances). Forexample, “substantially constant” includes “constant.” Unless statedotherwise, all measurements are at standard conditions includingtemperature and pressure.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y, and atleast one of Z to each be present.

1. A computer-implemented method of generating an immersivepatient-specific report on cardiovascular disease state based on one ormore plaque parameters derived from non-invasive medical image analysis,the method comprising: accessing, by a computer system, a medical imageof a subject, wherein the medical image of the subject is obtainednon-invasively; analyzing, by the computer system, the medical image ofthe subject to identify one or more arteries, wherein the one or morearteries comprise one or more regions of plaque; determining, by thecomputer system, one or more vascular parameters associated with thesubject by analyzing the one or more identified arteries, wherein theone or more vascular parameters comprise one or more of vessel volume,diameter, area, cross-sectional area, surface area, length, location, orremodeling; identifying, by the computer system, the one or more regionsof plaque in the one or more arteries; determining, by the computersystem, one or more plaque parameters associated with the one or moreregions of plaque, wherein the one or more plaque parameters compriseone or more of plaque density, composition, calcification, radiodensity,location, volume, surface area, geometry, heterogeneity, diffusivity, orratio between volume and surface area; generating, by the computersystem, an assessment of cardiovascular disease state of the one or moreregions of plaque based at least in part on the determined one or moreplaque parameters and the one or more vascular parameters; generating,by the computer system, a three-dimensional graphical representation ofone or more plaque rupture scenarios of the one or more regions ofplaque when the generated assessment of cardiovascular disease state ofthe one or more regions of plaque is above a pre-determined thresholdlevel; generating, by the computer system, a three-dimensionalmultiplanar reformation of the one or more arteries comprising the oneor more regions of plaque based at least in part on the determined oneor more vascular parameters and the one or more plaque parameters;generating, by the computer system, an immersive three-dimensionalgraphical representation of the one or more arteries based at least inpart on the three-dimensional multiplanar reformation of the one or morearteries, wherein the immersive three-dimensional graphicalrepresentation of the one or more arteries comprises thethree-dimensional graphical representation of the one or more plaquerupture scenarios of the one or more regions of plaque when thegenerated assessment of cardiovascular disease state of the one or moreregions of plaque is above a pre-determined threshold level; andcausing, by the computer system, transmission of the immersivethree-dimensional graphical representation of the one or more arteriesto a user computing device, wherein the immersive three-dimensionalgraphical representation of the one or more arteries is configured toallow a user to view the state of cardiovascular disease from a point ofview positioned inside the one or more arteries, wherein the computersystem comprises a computer processor and an electronic storage medium.2. The computer-implemented method of claim 1, wherein the usercomputing device comprises a virtual reality (VR) device.
 3. Thecomputer-implemented method of claim 1, wherein the pre-determinedthreshold level is based at least in part on density of the one or moreregions of plaque.
 4. The computer-implemented method of claim 1,wherein the density of the one or more regions of plaque comprisesabsolute density.
 5. The computer-implemented method of claim 1, whereinthe density of the one or more regions of plaque comprises radiodensity.6. The computer-implemented method of claim 1, wherein the immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow a user to move the point of view within the oneor more arteries in six degrees of freedom.
 7. The computer-implementedmethod of claim 1, wherein the immersive three-dimensional graphicalrepresentation of the one or more arteries is configured to allow a userto rotate the point of view within the one or more arteries in threedegrees of freedom.
 8. The computer-implemented method of claim 1,wherein the immersive three-dimensional graphical representation of theone or more arteries is configured to allow a user to move the point ofview within the one or more arteries along a longitudinal axis of theone or more arteries.
 9. The computer-implemented method of claim 1,further comprising generating, by the computer system, a treatment forcardiovascular disease for the subject based at least in part on thedetermined assessment of the state of cardiovascular disease of the oneor more regions of plaque.
 10. The computer-implemented method of claim9, further comprising: determining, by the computer system, an expectedprogression of the state of cardiovascular disease of the one or moreregions of plaque based on the treatment; modifying, by the computersystem, the immersive three-dimensional graphical representation of theone or more arteries based at least in part on the expected progressionof the state of cardiovascular disease of the one or more regions ofplaque; and causing, by the computer system, transmission of themodified immersive three-dimensional graphical representation of the oneor more arteries to the user computing device, wherein the modifiedimmersive three-dimensional graphical representation of the one or morearteries is configured to allow the user to view the expectedprogression of the state of cardiovascular disease of the one or moreregions of plaque from the point of view positioned within the one ormore arteries.
 11. The computer-implemented method of claim 9, whereinthe treatment for cardiovascular disease comprises medical intervention,medical treatment, or lifestyle change.
 12. The computer-implementedmethod of claim 9, further comprising tracking, by the computer system,efficacy of the treatment by determining assessment of the state ofcardiovascular disease of the one or more regions of plaque at a laterpoint in time after treatment.
 13. The computer-implemented method ofclaim 12, further comprising: modifying, by the computer system, theimmersive three-dimensional graphical representation of the one or morearteries based at least in part on the assessment of the state ofcardiovascular disease of the one or more regions of plaque at the laterpoint in time after treatment of the state of cardiovascular disease;and causing, by the computer system, transmission of the modifiedimmersive three-dimensional graphical representation of the one or morearteries to the user computing device, wherein the modified immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow the user to view a change in the state ofcardiovascular disease of the one or more regions of plaque aftertreatment from the point of view positioned within the one or morearteries.
 14. The computer-implemented method of claim 1, wherein thestate of assessment of cardiovascular disease state of the one or moreregions of plaque based at least in part on a weighted measure of theone or more plaque parameters and the one or more vascular parameters.15. The computer-implemented method of claim 1, further comprising:generating, by the computer system, a weighted measure of one or more ofthe one or more vascular parameters and the one or more plaqueparameters, wherein the assessment of the state of cardiovasculardisease of the one or more regions of plaque is further determined basedat least in part on the generated weighted measure.
 16. Thecomputer-implemented method of claim 1, wherein the assessment of thestate of cardiovascular disease of the one or more regions of plaque isfurther determined based at least in part on one or more of age orgender of the subject.
 17. The computer-implemented method of claim 1,wherein the medical image is obtained using an imaging techniquecomprising one or more of computed tomography (CT), x-ray, ultrasound,echocardiography, intravascular ultrasound (IVUS), MR imaging, opticalcoherence tomography (OCT), nuclear medicine imaging, positron-emissiontomography (PET), single photon emission computed tomography (SPECT), ornear-field infrared spectroscopy (NIRS).
 18. A system for generating animmersive patient-specific report on cardiovascular disease state basedon one or more plaque parameters derived from non-invasive medical imageanalysis, the system comprising: one or more computer readable storagedevices configured to store a plurality of computer executableinstructions; and one or more hardware computer processors incommunication with the one or more computer readable storage devices andconfigured to execute the plurality of computer executable instructionsin order to cause the system to: access a medical image of a subject,wherein the medical image of the subject is obtained non-invasively;analyze the medical image of the subject to identify one or morearteries, wherein the one or more arteries comprise one or more regionsof plaque; determine one or more vascular parameters associated with thesubject by analyzing the one or more identified arteries, wherein theone or more vascular parameters comprise one or more of vessel volume,diameter, area, cross-sectional area, surface area, length, location, orremodeling; identify the one or more regions of plaque in the one ormore arteries; determine one or more plaque parameters associated withthe one or more regions of plaque, wherein the one or more plaqueparameters comprise one or more of plaque density, composition,calcification, radiodensity, location, volume, surface area, geometry,heterogeneity, diffusivity, or ratio between volume and surface area;generate an assessment of cardiovascular disease state of the one ormore regions of plaque based at least in part on the determined one ormore plaque parameters and one or more vascular parameters; generate athree-dimensional graphical representation of one or more plaque rupturescenarios of the one or more regions of plaque when the generatedassessment of cardiovascular disease state of the one or more regions ofplaque is above a pre-determined threshold level; generate athree-dimensional multiplanar reformation of the one or more arteriescomprising the one or more regions of plaque based at least in part onthe determined one or more vascular parameters and the one or moreplaque parameters; generate an immersive three-dimensional graphicalrepresentation of the one or more arteries based at least in part on thethree-dimensional multiplanar reformation of the one or more arteries,wherein the immersive three-dimensional graphical representation of theone or more arteries comprises the three-dimensional graphicalrepresentation of the one or more plaque rupture scenarios of the one ormore regions of plaque when the generated assessment of cardiovasculardisease state of the one or more regions of plaque is above apre-determined threshold level; and cause transmission of the immersivethree-dimensional graphical representation of the one or more arteriesto a user computing device, wherein the immersive three-dimensionalgraphical representation of the one or more arteries is configured toallow a user to view the state of cardiovascular disease from a point ofview positioned inside the one or more arteries.
 19. The system of claim18, wherein the user computing device comprises a virtual reality (VR)device.
 20. The system of claim 18, wherein the pre-determined thresholdlevel is based at least in part on density of the one or more regions ofplaque.
 21. The system of claim 18, wherein the density of the one ormore regions of plaque comprises absolute density.
 22. The system ofclaim 18, wherein the density of the one or more regions of plaquecomprises radiodensity.
 23. The system of claim 18, wherein theimmersive three-dimensional graphical representation of the one or morearteries is configured to allow a user to move the point of view withinthe one or more arteries in six degrees of freedom.
 24. The system ofclaim 18, wherein the immersive three-dimensional graphicalrepresentation of the one or more arteries is configured to allow a userto rotate the point of view within the one or more arteries in threedegrees of freedom.
 25. The system of claim 18, wherein the immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow a user to move the point of view within the oneor more arteries along a longitudinal axis of the one or more arteries.26. The system of claim 18, wherein the system is further caused togenerate a treatment for cardiovascular disease for the subject based atleast in part on the determined assessment of the state ofcardiovascular disease of the one or more regions of plaque.
 27. Thesystem of claim 26, wherein the system is further caused to: determinean expected progression of the state of cardiovascular disease of theone or more regions of plaque based on the treatment; modify theimmersive three-dimensional graphical representation of the one or morearteries based at least in part on the expected progression of the stateof cardiovascular disease of the one or more regions of plaque; andcause transmission of the modified immersive three-dimensional graphicalrepresentation of the one or more arteries to the user computing device,wherein the modified immersive three-dimensional graphicalrepresentation of the one or more arteries is configured to allow theuser to view the expected progression of the state of cardiovasculardisease of the one or more regions of plaque from the point of viewpositioned within the one or more arteries.
 28. The system of claim 26,wherein the treatment for cardiovascular disease comprises medicalintervention, medical treatment, or lifestyle change.
 29. The system ofclaim 26, wherein the system is further caused to track efficacy of thetreatment by determining assessment of the state of cardiovasculardisease of the one or more regions of plaque at a later point in timeafter treatment.
 30. The system of claim 29, wherein the system isfurther caused to: modify the immersive three-dimensional graphicalrepresentation of the one or more arteries based at least in part on theassessment of the state of cardiovascular disease of the one or moreregions of plaque at the later point in time after treatment of thestate of cardiovascular disease; and cause transmission of the modifiedimmersive three-dimensional graphical representation of the one or morearteries to the user computing device, wherein the modified immersivethree-dimensional graphical representation of the one or more arteriesis configured to allow the user to view a change in the state ofcardiovascular disease of the one or more regions of plaque aftertreatment from the point of view positioned within the one or morearteries.
 31. The system of claim 18, wherein the state of assessment ofcardiovascular disease state of the one or more regions of plaque basedat least in part on a weighted measure of the one or more plaqueparameters and the one or more vascular parameters.
 32. The system ofclaim 18, wherein the system is further caused to: generate a weightedmeasure of one or more of the one or more vascular parameters and theone or more plaque parameters, wherein the assessment of the state ofcardiovascular disease of the one or more regions of plaque is furtherdetermined based at least in part on the generated weighted measure. 33.The system of claim 18, wherein the assessment of the state ofcardiovascular disease of the one or more regions of plaque is furtherdetermined based at least in part on one or more of age or gender of thesubject.
 34. The system of claim 18, wherein the medical image isobtained using an imaging technique comprising one or more of computedtomography (CT), x-ray, ultrasound, echocardiography, intravascularultrasound (IVUS), MR imaging, optical coherence tomography (OCT),nuclear medicine imaging, positron-emission tomography (PET), singlephoton emission computed tomography (SPECT), or near-field infraredspectroscopy (NIRS).